Grouping Genetic Algorithm for Solving the Server Consolidation ...

Grouping Genetic Algorithm for Solving the Server 

Consolidation Problem with Conflicts 

Shubham Agrawal 

Operations Research and Industrial 

Engg., Dept. of Mechanical Engg. 

University of Texas at Austin 

Austin, Texas, USA 

shubham@mail.utexas.edu 

Sumit Kumar Bose 

Distributed Computing Lab 

Software Engg. & Technology Labs 

Infosys Technologies Ltd, 

Bangalore, India 

sumit_bose@infosys.com 

Srikanth Sundarrajan 

Distributed Computing Lab 

Software Engg. & Technology Labs 

Infosys Technologies Ltd, 

Bangalore, India 

srikanth_sundarrajan@infosys.com 

ABSTRACT 

The advent of virtualization technologies encourages 

organizations to undertake server consolidation exercises for 

improving the overall server utilization and for minimizing the 

capacity redundancy within data-centers. Identifying 

complimentary workload patterns is a key to the success of server 

consolidation exercises and for enabling multi-tenancy within 

data-centers. Existing works either do not consider 

incompatibility constraints or performs poorly on the disjointed 

conflict graphs. The algorithm proposed in the current work 

overcomes the limitations posed by the existing solutions. The 

current work models the server consolidation problem as a vector 

packing problem with conflicts (VPC) and tries to minimize the 

number of servers used for hosting applications within datacenters 

and maximizes the packing efficiency of the servers 

utilized. This paper solves the problem using techniques inspired 

from grouping genetic algorithm (GGA) - a variant of the 

traditional Genetic Algorithm (GA). The algorithm is tested over 

varying scenarios which show encouraging results 

Evolved Finite State Controller for Hybrid System 

Jean-François Dupuis 

Technical University of 

Denmark 

Nils Koppels Alle, Building 424 

2800 Kgs. Lyngby, Denmark 

jfd@mek.dtu.dk 

Zhun Fan 

Technical University of 

Denmark 

Nils Koppels Alle, Building 424 

2800 Kgs. Lyngby, Denmark 

zf@mek.dtu.dk 

Erik Goodman 

Michigan State University 

2120 Engineering Building

East Lansing, MI, USA 48824 

goodman@egr.msu.edu 

ABSTRACT 

This paper presents an evolutionary methodology to automatically 

generate _nite state automata (FSA) controllers 

to control hybrid systems. FSA controllers for a case study 

of two-tank system have been successfully obtained using 

the proposed evolutionary approach. Experimental results 

show that these controllers have good performance on the 

set of training targets as well as on a randomly generated 

set of validation targets. 

An improved Simulated Annealing Algorithm for Vector 

Quantizer Design 

Mengyu Zhu 

Department of Electronic Engineering, Beijing Institute of 

Technology 

5 South Zhongguancun Street, Beijing, 100081, China 

zmy@bit.edu.cn 

Yuliang Yang 

Department of Communication Engineering, 

School of Information Engineering, 

University of Science and Technology Bejing 

30 Xueyuan Road, Beijing 100081, China 

Beijing, China 

teacheryangustb@126.com 

ABSTRACT 

An improved Simulated Annealing algorithm in conjunction with 

GLA algorithm has been proposed in this paper. Using SA 

algorithm and new distortion measure, our new algorithm can 

avoid the GLA algorithm's defect in that is sensitive to the 

original codebook and is easy to fall into the locally optimal 

solution during the searching. The experiment results indicate that 

the improved algorithm can efficiently eliminate the sensibility to 

the original codebook, and improve performance for searching 

ability and subjective quality of decoding image. 

Optimizing Constrained Non-convex NLP Problems 

in Chemical Engineering Field by a Novel Modified Goal 

Programming Genetic Algorithm 

Cuiwen Cao 

East China Univ. of Sci. 

& Tech. 

130 Meilong Road 

Xuhui District, Shanghai, China 

86-21-64252576 

caocuiwen@ecust.edu.cn 

Jinwei Gu 

East China Univ. of Sci. & Tech. 



86-21-64252576 

gujinwei1982@163.com 

Bin Jiao 

Shanghai Dianji Univ.

690 Jiangchuang Road 

Minhang District, Shanghai, 

China 

86-21-54758615 

binjiaocn@163.com 

Zhong Xin 




86-21-64252576 

xzh@ecust.edu.cn 

Xingsheng Gu* 




86-21-64252576 

xsgu@ecust.edu.cn 

ABSTRACT 

A novel modified goal programming genetic algorithm (MGPGA) 

is presented in this paper to solve constrained non-convex 

nonlinear programming (NLP) problems. This new method 

eliminates the complex equality constraints from original model 

and transforms them as parts of goal functions with higher priority 

weighting factors. At the same time, the original objective 

function has the lowest priority weighting factor. After all the 

absolute deviations of these equality constraints objectives are 

minimized, the final optimized solutions can be gained. Some 

applications in chemical engineering field are tested by this 

MGPGA. The proposed MGPGA demonstrates its advantages in 

better performances and abilities of solving non-convex NLP 

problems especially for those with equality constraints 

Search-based Multi-paths Test Data Generation for 

Structure-oriented Testing 

Yang Cao 

Tsinghua University 

School of Aerospace 


y-cao@mails.tsinghua.edu.cn 

Chunhua Hu 




huchuhua@tsinghua.edu.cn 

Luming Li 




lilm@tsinghua.edu.cn 

ABSTRACT 

This paper presents a new fitness function to generate test data 

for a specific single path, which is different from the predicate 

distance applied by most test data generators based on genetic 

algorithms (GAs). We define a similarity between the target path 

and execution path to evaluate the quality of the populations. The 

problem of the most existing generators is to search only one 

target data a time, wasting plenty of available interim data. We 

construct another fitness function combined with the single path

function, which can drive GA to complete covering multi-paths to 

avoid the reduplicate searching and utilize the interim populations 

for different paths. 

Several experiments are taken to examine the effectiveness of 

both the single path and multi-path fitness functions, which 

evaluate the functions’ performance with the convergence ability 

and consumed time. Results show that the two functions perform 

well compared with other two typical path-oriented functions and 

the multi-paths approach retrenches the searching actually 

A Hybrid Neural-genetic Approach for Reconfigurable 

Scheduling of Networked Control System 

Hui Chen 

Key Laboratory of Ministry 

of Education for Image Processing 

& Intelligent Control 

Dept. of Control Science 

& Engineering, 

Huazhong University of Science 

and Technology 

Wuhan, Hubei, China, 430074 

Phone: +86 027 87558001 

husthuichen@gmail.com 

Chunjie Zhou 









Phone: +86 027 87558001 

cjiezhou@sina.com 

Weifeng Zhu 









Phone: +86 027 87558001 

hustzhuwf@tom.com 

ABSTRACT 

In this paper, a novel approach for networked control system 

(NCS) task scheduling is proposed. The proposed neural-genetic 

method utilizes the information about the quality of service (QoS) 

over the communication network and enables online reconfigurable 

scheduling on distributed environment. In this way the NCS’s 

bandwidth can be shared properly among different parallel control 

tasks. For NCS, two significant factors of QoS that affect validity 

of scheduling results are the packet loss and delay, which occurred 

in the communication among tasks. By adopting a Elman neural 

network based prediction model, the one-step ahead packet loss

and time delay are obtained. The knowledge about the predict QoS 

factors, combined with the task execution features and the 

resources available in the system, are used as an entry to improve 

the decisions of the proposed scheduling algorithm. Such algorithm 

uses genetic algorithm techniques to find out the appropriate task 

scheduling scheme to adapt changes of application and 

communication circumstance. The proposed neural-genetic 

approach is evaluated through simulation by using a model 

parameterized with the features obtained from a real scenario of 

Ethernet based control system. The simulation results clearly show 

the effectiveness of the proposed approach in solving the task 

scheduling problems in NCS. 

A Genetic Approach to Channel Assignment for Multiradio 

Multi-channel Wireless Mesh Networks 

Jian Chen, Jie Jia 

School of Information Science and 

Engineering 

Northeastern University 

Shenyang 110004, China 

chen.jian@neusoft.com 

Yingyou Wen 


Engineering 



wenyy@neusoft.com 

Dazhe Zhao, Jiren Liu 

National Engineering Research 

Center for Computer Software 



zhaodz@neusoft.com 

ABSTRACT 

Multi-channel communication in a Wireless Mesh Network with 

routers having multiple radio interfaces significantly enhances the 

network capacity. Efficient channel assignment is critical for 

realization of optimal throughput in such networks. In this paper, 

we investigate the problem of finding the largest number of links 

that can be connected with the overall network interference is 

minimized. Since the number of radios on any node can be less 

than the number of available channels, the channel assignment 

must obey the constraint that the number of different channels 

assigned to the links incident on any node is at most the number 

of radio interfaces on that node. The above optimization problem 

is known to be NP-hard. By presenting the theoretical model, the 

above task is formulated as a multi-objective problem, and then a 

novel channel assignment based on improved NSGA-II is 

proposed. Extensive empirical evaluations represent that the novel 

algorithm proposed in this paper can implement network 

connectivity with little interference rapidly and efficiently. To 

meet the actual demand in wireless mesh network, ns-2 

simulations are used to demonstrate the performance potential of 

our channel assignment algorithms in 802.11-based multi-radio 

mesh networks.

Modeling and Extending Lifetime of Wireless Sensor 

Networks Using Genetic Algorithm 

Jian Chen, Jie Jia 


Engineering 



chen.jian@neusoft.com 

Yingyou Wen 


Engineering 



wenyy@neusoft.com 

Dazhe Zhao, Jiren Liu 

National Engineering Research 

Center for Computer Software 



zhaodz@neusoft.com 

ABSTRACT 

To extend the lifetime of the sensor networks as far as possible 

while maintaining the quality of network coverage is a major 

concern in the research of coverage control. A systematical 

analysis on the relationship between the network lifetime and 

cover sets alternation is given, and by introducing the concept of 

time weight factor, the network lifetime maximization model is 

presented. Through the introduction of the solution granularity 

ΔT, the network lifetime optimization problem is transformed into 

the maximization of cover sets. A solution based on NSGA-II is 

proposed. Compared with the previous method, which has the 

additional requirement that the cover sets being disjoint and 

results in a large number of unused nodes, our algorithm allows 

the sensors to participate in multiple cover sets, and thus makes 

fuller use of the whole sensor nodes to further increase the 

network lifetime. Simulation results are presented to verify these 

approaches. 

Particle Swarm Optimization Algorithm Based on Dynamic 

Memory Strategy 

Qiong Chen 

School of Computer Science 

and Technology, Wuhan 

University of Technology, 

Wuhan 430070, China 

ch-chong@hotmail.com 

Shengwu Xiong 

¤ 





xiongsw@whut.edu.cn 

Hongbing Liu





liuhbing@sohu.com 

ABSTRACT 

This paper mainly studies the in°uence of memory on indi- 

vidual performance in particle swarm system. Based on the 

observation of social phenomenon from the perspective of 

social psychology, the concept of individual memory contri- 

bution is de¯ned and several measurement methods to deter- 

mine the level of e®ect of individual memory on its behavior 

are discussed. A dynamic memory particle swarm optimiza- 

tion algorithm is implemented by dynamically assigning ap- 

propriate weight to each individual's memory according to 

the selected metrics values. Numerical experiment results on 

benchmark optimization function set show that the proposed 

scheme can e®ectively adjust the weight of individual mem- 

ory according to di®erent optimization problems adaptively. 

Numerical results also demonstrate that dynamic memory is 

an e®ective improvement strategy for preventing premature 

convergence in particle swarm optimization algorithm. 

The Non-Clique Particle Swarm Optimizer 

Ziyu Chen 

College of Computer Science 

Chongqing University 

Chongqing 400044, China 

chenziyu@cqu.edu.cn 

Zhongshi He 




zshe@cqu.edu.cn 

Cheng Zhang 




zc0_0@163.com 

ABSTRACT 

Neighborhood topology of particle swarm affects the performance 

of PSO. Through analyzing the graph properties of typical 

neighborhood topologies, this paper presents a non-clique static 

neighborhood topology which has lower clustering coefficient and 

smaller average path length. Compared to other topologies with 

the same neighborhood size and population size, the proposed 

topology has more uniform neighbor distribution. The experiment 

results demonstrate that the PSO based on the non-clique 

topology has great superiority both in robustness and efficiency. 

、 

A Co-Evolutionary Approach for Military Operational 

Analysis 

Choo, Chwee Seng 

DSO National Laboratories

20, Science Park Drive 

Singapore 118230 

65-67727125 

cchweese@dso.org.sg 

Chua, Ching Lian 

DSO National Laboratories 



65-67727376 

chuacl@dso.org.sg 

Low, Kin Ming Spencer 




65-67727376 

lkinming@dso.org.sg 

Ong, Wee Sze Darren 

Defence Science and 

Technology Agency 



65-68795077 

oweesze@dsta.gov.sg 

ABSTRACT 

In this paper, we describe Automated Co-Evolution (ACE), a 

framework that uses Competitive Co-Evolutionary Algorithm 

(CCEA) and High Performance Computing (HPC), to study the 

dynamics of competition in a military context through 

simulations. The overall goal is to complement the manually 

intensive actions-reactions process in developing (automatically) 

a Blue plan that performs well and is relatively robust even in the 

face of an adaptive Red adversary. The design of key components 

and techniques that are required to develop the ACE framework 

are described and discussed. An academic study using a military 

scenario - Maritime Anchorage Protection, was conducted and the 

results analyzed to demonstrate the capability of the ACE 

framework. It also illustrated how the ACE process could be used 

to complement Operational Analysis (OA). 

Evolving Common LISP Programs in a Linear-Genotype 

Evolutionary Computation System 

Jamie S. Cullen 

Artificial Intelligence Laboratory 

University of New South Wales, Sydney NSW 

jsc@cse.unsw.edu.au 

ABSTRACT 

Evolutionary Meta Programming (EMP) is an approach to 

Evolutionary Computation, which allows freedom of pro- 

gramming language choice in the evolved programs, as well 

as the ready use of external tools and testbenches, with 

which to perform fitness evaluation. The current implemen- 

tation of EMP uses a linear genotype in a manner simi- 

lar to Grammatical Evolution (GE). In contrast, traditional 

Genetic Programming (GP) typically uses a subset of the 

LISP programming language to represent target programs 

in a tree-based structure. The ability of EMP to leverage 

external tools and arbitrary languages enables the rapid pro-

totyping of possibly novel approaches to Evolutionary Com- 

putation. One such experiment is presented herein: The 

evolution of Common LISP language constructs using a lin- 

ear genotype and associated grammar, and evaluation using 

a real external LISP interpreter. An exploratory study is 

performed with three classic problems: Symbolic Regres- 

sion, Ant Trail, and Towers of Hanoi. Solutions to these 

problems were evolved in both Common LISP and ANSI 

C versions, and runtime and performance results collected. 

Present results are relatively unintuitive, when compared 

to conventional programming wisdom, with some problems 

apparently favoring a paradigm not traditionally suited to 

them in a non-evolutionary programming setting. 

Ant Colony Optimization for Precedence-Constrained 

Heterogeneous Multiprocessor Assignment Problem 

Rong Deng 

Department of Computer Science and 

Engineering, Tongji University, 

4800 Caoan Road, 

ShangHai,China,201804 

86+21+69589864 

drong2004@tongji.edu.cn 

Changjun Jiang 





86+21+69589864 

cjjiang@tongji.edu.cn 

Fei Yin 





86+21+69589864 

hhaafy@tongji.edu.cn 

ABSTRACT 

An ant colony optimization approach, named MPAACO, for the 

Precedence-Constrained Heterogeneous Multiprocessor 

Assignment Problem (PCHMAP) is presented. The main 

characteristics of MPAACO are novel pheromone matrix and 

solution construction scheme. Separating processor selection steps 

from task selection steps, ant colony has full flexibility to 

construct new solution. Three-dimensional pheromone matrix can 

record each solution construction step precisely. When combined 

with heuristic information, they endow MPAACO the ability to 

find high quality schedules of PCHMAP quickly. We tested the 

algorithm on a set of benchmark problems from the [18]. The 

result shows that for 77% of all benchmark for Precedence- 

Constrained Homogeneous Multiprocessor Assignment Problem, 

a special case of PCHMAP, the algorithm can get the optimal in 

just one try. For PCHMAP problems, MPAACO outperforms 

other algorithms significantly. 

Genetic Programming for Quantitative Stock Selection

Ying L. Becker 

US/Global Active Equity Research 

State Street Global Advisors 

Boston, MA 02111 

(617) 664 - 2907 

Ying_Becker@ssga.com 

Una-May O’Reilly 

CSAIL 

Massachusetts Institute of Technology 

Cambridge, MA, USA 

(617) 253 - 6437 

unamay@csail.mit.edu 

ABSTRACT 

We provide an overview of using genetic programming (GP) to 

model stock returns. Our models employ GP terminals (model 

decision variables) that are financial factors identified by experts. 

We describe the multi-stage training, testing and validation 

process that we have integrated with GP selection to be 

appropriate for financial panel data and how the GP solutions are 

situated within a portfolio selection strategy. We share our 

experience with the pros and cons of evolved linear and nonlinear 

models, and outline how we have used GP extensions to 

balance different objectives of portfolio managers and control the 

complexity of evolved models. 

Multi-Strategy Grouping Genetic Algorithm for the Pickup 

and Delivery Problem with Time Windows 

Ding Genhong 

Hohai University 

Nanjing, 210098 

People’s Republic of China 

86-25-83786626 

dinggenhong@126.com 

Li Linye 




86-15850589547 

lyxueping@163.com 

Ju Yao 




86-15950523806 

ericarenas@163.com 

ABSTRACT 

The Pickup and Delivery Problem with Time Windows (PDPTW) 

is a generalization of the well studied Vehicle Routing Problem 

with Time Windows (VRPTW). This paper studies a Grouping 

Genetic Algorithm for solving the PDPTW. The insertionsearching 

heuristics (in GGA) which can generate feasible 

solutions was improved, new data structures were built, and then 

three routing adjustment strategies were added to come up with 

the Multi-Strategy Grouping Genetic Algorithm (MSGGA). The 

PDPTW benchmark problems with 100 customers are calculated 

with MSGGA, and the comparison between the result and that of 

the reference shows that the new algorithm shortens the 

calculating time with its astringency, better solutions of four cases

are obtained and stability is improved. 

Self-Fertilization Based Genetic Algorithm for University 

Timetabling Problem 

Zan Wang 

School of Management of Tianjin 

University, Information and Network 

Center of Tianjin University 

Room 208, Information and Network 

Center of Tianjin University, Tianjin, 

China 

(+8622)27401113 

wangzan@tju.edu.cn 

Jin-lan Liu 


University 

Room 220, Building 9th of Tianjin 

University, Tianjin, China 

(+8622)87401927 

liujinlan@tju.edu.cn 

Xue Yu 


University 

Room A-1108, Building 25th of Tianjin 


(+8622)27401021 

yuki@tju.edu.cn 

ABSTRACT 

In this paper, a new algorithm inspired from the self-fertilization 

of some plants is proposed for the university timetabling problem 

(UTP). The main idea of the algorithm is to modify the fitness 

function, the selection and crossover operators of GA to obtain a 

further fit for UTP. Fitness function of this algorithm will neglect 

hard constraints because no infeasible individual can pass the 

check of advisor to survive. The advisor based on heuristic 

methods can also simplify the computation once there are changes 

on constraints. Distinguished from traditional crossover, a new 

exchange in one chromosome rather than between chromosomes 

will be issued to keep the integrity of the schedule. During some 

processes, simulated annealing was introduced as a select strategy 

for diversity of the population. This algorithm was implemented 

and tested with the real data of Tianjin University, China. The 

algorithm produces good timetable for the students and teachers 

and improve the usage rate of classroom. The experiment results 

indicate that our new hybrid genetic algorithm that addressing 

timetabling problem is promising and converge rapidly. 

Self-Fertilization Based Genetic Algorithm for University 

Timetabling Problem 

Zan Wang 


University, Information and Network 

Center of Tianjin University 

Room 208, Information and Network 

Center of Tianjin University, Tianjin,

China 

(+8622)27401113 

wangzan@tju.edu.cn 

Jin-lan Liu 


University 

Room 220, Building 9th of Tianjin 


(+8622)87401927 

liujinlan@tju.edu.cn 

Xue Yu 


University 

Room A-1108, Building 25th of Tianjin 


(+8622)27401021 

yuki@tju.edu.cn 

ABSTRACT 

In this paper, a new algorithm inspired from the self-fertilization 

of some plants is proposed for the university timetabling problem 

(UTP). The main idea of the algorithm is to modify the fitness 

function, the selection and crossover operators of GA to obtain a 

further fit for UTP. Fitness function of this algorithm will neglect 

hard constraints because no infeasible individual can pass the 

check of advisor to survive. The advisor based on heuristic 

methods can also simplify the computation once there are changes 

on constraints. Distinguished from traditional crossover, a new 

exchange in one chromosome rather than between chromosomes 

will be issued to keep the integrity of the schedule. During some 

processes, simulated annealing was introduced as a select strategy 

for diversity of the population. This algorithm was implemented 

and tested with the real data of Tianjin University, China. The 

algorithm produces good timetable for the students and teachers 

and improve the usage rate of classroom. The experiment results 

indicate that our new hybrid genetic algorithm that addressing 

timetabling problem is promising and converge rapidly. 

Research on Stronger Convergence in Probability 

of Immune Genetic Algorithm 

Luo Xiaoping 

Zhejiang University City College 

Department of Electrical Engineering 

Hangzhou,Zhejiang,310015, 

P.R.China 

luoxp@zucc.edu.cn 

Peng Yonggang* 

College of Electrical Engineering 

Zhejiang University 


P.R.China 

pengyg@zju.edu.cn 

Wei Wei 




P.R.China 

wwei@zju.edu.cn 

ABSTRACT

Immune Genetic Algorithm (IGA) is a new optimization strategy 

by simulating the behavior of biological immune system. Aiming 

at the relatively scarce work on the discussion of convergence on 

IGA, strong convergence in probability of IGA was proved on the 

condition that the time tended to infinity comparing to the 

previous conclusion that IGA was weak convergence in 

probability by (1)modeling the immune operators and 

optimization process and (2)introducing a lemma with 2 immune 

parameters to analyze some characteristics of the complement set 

of global optima set. This conclusion will be helpful to understand 

the performance of IGA and set better immune parameters. 

To Create Neuro-Controlled Game Opponent from UCTCreated 

Data 

Fan Xie, Suoju He, Xiao Liu, Xingguo Li, Junping Du, Jiajian Yang, Yiwen Fu, Yang Chen, 

Junping Wang, Zhiqing Liu, Qiliang Zhu 

Beijing University of Posts and Telecommunications, Beijing, China, 100876 

xiefan198877@gmail.com, suojuhe@yahoo.ca 

ABSTRACT 

Adaptive Game AI improves adaptability of opponent AI as well 

as the challenge level of the gameplay, as a result the 

entertainment of game is augmented. Opponent game AI is 

usually implemented by scripted rules in video games, but the 

most updated algorithm of UCT (Upper Confidence bound for 

Trees) which perform well in computer go can also be used to 

achieve excellent result to control non-player characters (NPCs) 

in video games. However, due to computational intensiveness of 

UCT, it is actually not suitable for Online Games. As it is already 

known that UCT can create near optimal control, so it is possible 

to create Neuro-Controlled Game Opponent by off-line learning 

from the UCT created sample data; finally Neuro-Controlled 

Game Opponent for Online Games from UCT-Created Data 

without worry about computational intensiveness is generated. 

And also if the optimization approach of Neuro-Evolution is 

applied to the above generated Neuro-Controller, the performance 

of the opponent AI is enhanced even further. 

Efficient Annealing -Inspired Genetic Algorithm 

for Information Retrieval from Web-Document 

Yuan Xu 

Software School 

Dalian University of Technology, 

Dalian, Liaoning Province, 116023 

China 

+086-138-4084-6152 

nakusakula@gmail.com 

Yang Deli 

Software School, 

Dalian University of Technology 


China 

+086-138-4084-6152 


Liu Yu 

Software School 

Dalian University of Technology


China 

+086-138-4084-6152 


ABSTRACT 

With the huge amount of information available online, the World 

Wide Web is a fertile area for data mining research. The Web 

mining research is at the cross road of research from several 

research is at the cross road of research from several research 

communities. In this paper, a new adaptive method of mining web 

documents is proposed. We give an algorithm which combines 

genetic algorithm and simulated annealing based on vector space 

model. This algorithm avoids the disadvantage of web documents 

by using pure genetic algorithm which can not be utilized 

accurately .Experimental results indicate that this adaptive method 

significantly improves the performance in retrieval accuracy. 

Controlling Swarm Robots with Kinematic Constraints for 

Target Search 

Songdong Xue 

† Complex Syst. & Computational Int. Lab 

Taiyuan University of Science and Technology 

66 Waliu Rd., Taiyuan 030024, China 

‡ Col. of Elect. & Informat. Engn. 

Lanzhou University of Technology 

85 Langongping, Lanzhou 730050, China 

xuesongdong@gmail.com 

Jianchao Zeng 

Complex Syst. & Computational Int. Lab 

Taiyuan University of Science and Technology 

66 Waliu Rd., Taiyuan, Shanxi 030024, China 

zengjianchao@263.net 

ABSTRACT 

An approach to control artificial swarm whose members are 

autonomous wheeled mobile robots is proposed, by applying 

Particle Swarm Optimization (PSO) to target search. 

First, swarm search is mapped to PSO based on similarities 

between the two cases. Then a distributed PSO-style algorithm 

is given, in which decision making on real inputs of linear 

and angular velocity of robot controller being explored. 

We obtain the required command sequences by constraining 

the computational expected velocities and positions with 

robot’s non-holonomic properties in kinematics. In this way, 

swarm robots can work together cooperatively. 

Frame-layer Rate Control Algorithm 

for Multi-view Video Coding 

Tao Yan1,2 Liquan Shen1,2 Ping An1,2 He Wang1 Zhaoyang Zhang1,2 

1School of Communication and Information Engineering 

2Key Laboratory of Advanced Displays and System Application, Ministry of Education Shanghai University 

Shanghai, China 

021-56332183 

yantaoshu@yahoo.com.cn 

ABSTRACT 

Rate control has not been well studied for multi-view video 

coding (MVC). We propose a rate control algorithm for MVC

ased on the quadratic rate-distortion model. We remodel the 

quadratic rate-distortion model for multi-view videos based on the 

type of each frame. In the frame level, the quantization 

parameters are set according to the parameters of the various 

kinds of image model which is set up through the analysis of the 

coded information. The experimental results show that the 

proposed scheme can allocate the bits and control the rate 

efficiently. 

Research of Fuzzy Control Strategy on Artificial Climate 

Chest 

Yang Yang 

School of Automation, Hangzhou 

Dianzi University 

Hangzhou,Zhejiang,310018,P.R.China 

zjyangyang@gmail.com 

Luo Xiaoping* 


Hangzhou 

Zhejiang,310015,P.R.China 


Peng Yonggang, Wei Wei 




{pengyg,wwei}@zju.edu.cn 

ABSTRACT 

Aiming at the lack of effective control strategies about a 

nonlinear, strong coupling and long time delay object---artificial 

climate chest, a new adaptive control method is proposed based 

on fuzzy theory. An improved fuzzy controller which can selfadjust 

parameters on-line is designed. Furthermore, it is proved 

that the control strategy in this paper is effective and superior 

with fuzzy set theory, multi-variable Fourier Transform and 

approximate theory by analyzing the essential model of fuzzy 

controller. Last, the results of experiments show that the method 

proposed in this paper can control temperature and humidity in 

artificial climate chest better. The results of this paper can be 

helpful in understanding fuzzy control more deeply and directing 

how to design fuzzy controller for complicated systems. 

Optimal Multi-objective Design of Power System Damping 

Controller Using Synergy of Bacterial Forging 

and Particle Swarm Optimization 

Sun Yong 

Harbin Institute of Technology 

Harbin, China 

sunqiu126@sohu.com 

Li Zhimin 


Harbin, China 

lizhimin@hit.edu.cn

Zhang Dongsheng 


Harbin, China 

zhangds1977@126.com 

ABSTRACT 

In order to solve the parameter optimization problem of 

traditional power system stabilizer, a novel power system 

stabilizer (PSS) design method is proposed based on synergy of 

bacterial forging and particle swarm optimization algorithm. 

Bacterial foraging algorithm may lead to delay in reaching global 

solution. Particle swarm optimization may lead to entrapment in 

local minimum solution and obtain imprecise search results. The 

new algorithm is proposed to combines both algorithms’ 

advantages in order to get better optimization values. A 

coordinate optimization index based on multi-object and multiple 

operation conditions is presented so as to improve the damping 

ratios of electromechanical modes and increase the robustness of 

power system. In this paper, PSS design for single machine 

infinite bus is formulated as multi-objective and multi-operating 

conditions, and the hybrid approach involving bacterial foraging 

and particle swarm optimization algorithm is employed to solve 

this problem. The results of both eigenvalue analysis and 

nonlinear simulation show that the proposed PSS can damp the 

low-frequency oscillations effectively and work well with high 

control performance under different operating conditions. 

Compared with PSS which is design by genetic algorithm, the 

proposed PSS in this paper has better damping characteristics. 

Hyperchaotic Genetic Algorithm Theory and Functions 

Optimization 

You-Ming Yu 

Dept. of Computer 

Beijing Institute of Petrochemical 

Technology, Beijing 102617 China 

81292148,8610 

yuyouming@bipt.edu.cn 

Guo-Qing Zhao 

Dept. of Computer 



81292148,8610 

zhaoguoqing@bipt.edu.cn 

Jian-Dong Liu 

Dept. of InfoTechn. 



81292295,8610 

liujiandong@bipt.edu.cn 

ABSTRACT 

Genetic algorithm (GA) has premature limitation,so the 

hyperchaotic genetic algorithm (HCGA) was proposed. Applied a 

new chaos-genetic evolution mechanism for avoiding the 

repetition operation among the currently common chaos 

optimization and crossover operator and mutation operator during 

evolution process. Adopting hyperchaotic model based on 

coupled map lattices loading hyperchaotic variables on variables 

population of genetic algorithm, fulfilled no collision evolution 

and fast convergence by the small disturbance from hyperchaotic 

variable to subpopulation and adjusted adaptively disturbance

ange during search process. The function optimization results 

show HCGA improved the convergence and reduce the 

computing time greatly than GA or chaos genetic algorithm 

(CGA).Using the average truncated generation and the 

distribution entropy of truncated generations as evaluation 

criterion of optimization efficiency, compared quantificationally 

HCGA with CGA and GA, the optimization computation results 

show that the HCGA has higher optimization efficiency than 

CGA and GA. 

Cloud Service and Service Selection Algorithm Research 

Wenying Zeng 

School of Computer Science and 

Engineering, 

South China University of Technology; 

Guangdong Institute of Science and 

Technology 

Guangzhou 510640, China 

wyzeng@126.com 

Yuelong Zhao 

School of Computer Science and 

Engineering, 

South China University of Technology 

Guangzhou 510640, China 

ylzhao1@scut.edu.cn 

Junwei Zeng 

Chongqing University of Posts and 

Telecommunications 


jxzjw@126.com 

ABSTRACT 

This paper describes the cloud service architecture and key 

technologies for service selection algorithm. Cloud computing is a 

hot topic on software and distributed computing based on Internet, 

which means users can access storages and applications from 

remote servers by web browsers or other fixed or mobile 

terminals. Because the constrained resources of fixed or mobile 

terminals, cloud computing will provide terminals with powerful 

complementation resources to acquire complicated services. The 

paper discusses the cloud service architecture and key algorithms 

about service selection with adaptive performances and minimum 

cost. The cloud service architecture is reasonable and the 

proposed service selection algorithms are available, scalable, and 

adaptive to different types of environments of services and 

clients. 

Hybrid Differential Evolution and the Simplified Quadratic 

Interpolation for Global Optimization 

Li Zhang 

National Key Laboratory of Antennas and 

Microwave Technology 

Xidian University 

Xi’an, Shaanxi, 710071,China 

lizhang@mail.xidian.edu.cn 

Yong-Chang Jiao 

National Key Laboratory of Antennas and




ychjiao@xidian.edu.cn 

Hong Li 





lihong@mail.xidian.edu.cn 

Fu-Shun Zhang 





fshzhang@mail.xidian.edu.cn 

ABSTRACT 

To improve the searching ability and convergence speed of 

di®erential evolution (DE), we combined a search opera- 

tion for enhancing the performance of the original DE. The 

simpli¯ed quadratic interpolation (SQI) is employed to im- 

prove the local search ability and the accuracy of the min- 

imum function value, and to reduce greatly the computa- 

tional overhead of the algorithm. The classic benchmark 

test functions are employed to evaluate the performance of 

the proposed method. We also provide a comparison of 

the proposed method to fuzzy adaptive di®erential evolu- 

tion (FADE). Experimental results con¯rm that the pro- 

posed method outperforms the original DE and FADE in 

terms of convergence speed, solution quality, and solution 

stability. 

Object Segmentation Based on Disparity Estimation 

Qian Zhang1 Suxing Liu1 Ping An1,2 Zhaoyang Zhang1,2 

1School of Communication and Information Engineering 

2Key Laboratory of Advanced Displays and System Application, Ministry of Education Shanghai University 

Shanghai,China 

021-56332183 

anping@shu.edu.cn 

ABSTRACT 

Object segmentation plays an important role in multi-view video 

analysis. In this paper, we present a new object segmentation 

method for multi-view video in which only the disparity is used 

for segmentation and the motion estimation is neglected. Firstly, a 

modified locally adaptive support-weight approach is proposed 

for disparity estimation. Then, segmentation is realized by meanshift 

algorithm. The experimental results show that proposed 

method could segment the semantically meaningful objects from 

complex background with high precision. 

A Weight Based Compact Genetic Algorithm 

Qing-bin Zhang

Shijiazhuang Institute of Railway Technology Shijiazhuang 050041, China 

zqbin2002@sina.com 

Ti-hua Wu 

Hebei Academy of Sciences 46 South Youyi street Shijiazhuang 050081, China 

wuhas@heinfo.net 

Bo Liu 

Hebei Academy of Sciences 46 South Youyi street Shijiazhuang 050081, China 

q_water2003@yahoo.com 

ABSTRACT 

In order to improve the performance of the compact Genetic Algorithm(cGA) to solve difficult optimization problems, an improved cGA which 

named as the weight based compact Genetic Algorithm (wcGA) is proposed. In the wcGA, S individuals are generated from the probability vector in 

each generation, when the winner competing with the other S-1 individuals to update the probability vector, different weights are multiplied to each 

solution according to the sequence of the solution ranked in the S-1 individuals. Experimental results on three kinds of Benchmark functions show that 

the proposed algorithm has higher optimal precision than that of the standard cGA and the cGA simulating higher selection pressures. 

An Improved Differential Evolution to Continuous Domains 

and Its Convergence 

Yuntao Zhao 

National Engineer Research Center 

of Advanced Rolling, 

University of Science and Technology 

Beijing, Beijing, China 

zyt1013@126.com 

Jing Wang 





wangj@nercar.ustb.edu.cn 

Yong Song 





songyong@nercar.ustb.edu.cn 

ABSTRACT 

When differential evolution algorithm is applied in complicated 

optimization problems, it has the shortages of prematurity and 

stagnation. An improved differential evolution to obtain solutions 

quickly is proposed in this paper. The algorithm takes into 

account the information of problem solving and objective 

function. Firstly, a hybrid optimization strategy that parallelly 

executes uniform crossover and Binomial crossover is designed. 

So individuals can fully represent the solution space. Secondly, a 

transform function is constructed. This method is utilized to 

simplify the objective function .It eliminates local minimum and 

keeps the value of optimized function unchanged under the local 

minimum. Then its convergence is analyzed theoretically, and is 

proved to converge to the best solution. This algorithm is also 

tested by several benchmark functions. The simulation results 

show that it has perfect property in efficacy and converges faster

Study to Short-term Flow Estimation 

at Intersection Base on Genetic Neural Networks 

Zhou ZhiNa 

Air Traffic Management Institute, 

Northwestern Polytechnical 

University, Xi’an,710072,China 

zhina_zhou@126.com 

Shi ZhongKe 




Li YingFeng 




ABSTRACT 

The traffic flow data is the foundation of the transportation 

management and control. Inevitably there is data loss in traffic 

parameters acquisitions, so it needs traffic flow estimation to 

complete the traffic flow information when the data loss is serious. 

Proper estimation of traffic flow is an essential component of 

advanced management of dynamic traffic networks. The genetic 

nerve-network is developed, combined the nerve network and the 

genetic algorithm together, to estimate the short-term traffic 

volume. According to the experiment result, the method is 

effective to estimate traffic flow in the short term at intersection 

Study to Short-term Flow Estimation 

at Intersection Base on Genetic Neural Networks 

Zhou ZhiNa 




zhina_zhou@126.com 

Shi ZhongKe 




Li YingFeng 




ABSTRACT 

The traffic flow data is the foundation of the transportation 

management and control. Inevitably there is data loss in traffic 

parameters acquisitions, so it needs traffic flow estimation to 

complete the traffic flow information when the data loss is serious. 

Proper estimation of traffic flow is an essential component of 

advanced management of dynamic traffic networks. The genetic 

nerve-network is developed, combined the nerve network and the 

genetic algorithm together, to estimate the short-term traffic 

volume. According to the experiment result, the method is 

effective to estimate traffic flow in the short term at intersection

Independent Global Constraints for Web Service 

Composition Based on GA and APN 

Xianwen Fang1,2,3 

1Key Lab of Embedded System & 

Service Computing Ministry of 

Education, Tongji University, 

Shanghai, 201804, China 

+86-021-69589864 

xwenfang@tom.com 

Changjun Jiang1,2 

2Electronics and Information 

Engineering School, Tongji University, 

Shanghai, 201804, China 

+86-021-69589864 

cj-jiang@tongji.edu.cn 

Xiaoqin Fan1,2 

3Information Science Department, 

Anhui University of Science and 

Technology, Huainan, Anhui Province, 

232001, China 

+86-021-69589864 

fxq0917@hotmail.com 

ABSTRACT 

The Service composition has been a popular research presently. 

Service Composition by manual cannot meet the expectations in 

reality, but the wholly intellectualized automatic service 

composition is a very complicated process. So, many applications 

and research about service composition are oriented to semiautomatic 

service composition, for obtaining optimal performance 

by some compositing policies. 

A global constraint is independent if the values that should be 

assigned to all the remaining restricted attributes can not be 

uniquely determined once a value is assigned to one. Based on the 

Web service ontology, the paper presents an independent global 

constrains-aware Web service composition approach based on 

semantic. Associate Petri net (APN) modeling methods which can 

describe multi-attribute multi-constraint relations and associate 

relationships between component services are proposed. Then, 

using the properties and reasoning rules of APN, a constraintaware 

service composition optimization algorithm is presented in 

order to locate legal firing sequences in APN model, and those 

corresponding to the legal firing sequences with the biggest trust 

value are the optimal solutions. Lots of experiments show that this 

semantic-based method has both lower time consuming and 

higher success ratio of service composition. 

Tabu-search for Single Machine Scheduling With 

Controllable Processing Times 

Zuren Feng 

Xi’an Jiaotong University 

28 Xianning Lane 

Xi’an, China 

fzr9910@mail.xjtu.edu.cn 

Kailiang Xu 

Xi’an Jiaotong University 

28 Xianning Lane


xl.8046@stu.xjtu.edu.cn 

ABSTRACT 

In this paper, we consider scheduling n jobs with arbitrary 

release dates and due dates on a single machine, where jobs’ 

processing times can be controlled by the allocation of a 

common resource, and the operation is modeled by a non- 

linear convex resource consumption function. The objective 

is to obtain an optimal processing sequence as well as op- 

timal resource allocation, such that all the jobs could be 

finished no later than their due dates, and the resource con- 

sumption could be minimized. Since the problem is strongly 

NP-hard, a two-layer-structured algorithm based on tabu- 

search is presented. The computational result compared 

with a branch-and-bound algorithm showed the algorithm 

is capable for producing optimal and near optimal solution 

for large sized problems in acceptable computational time. 

Particle Swarm Optimization Algorithm for Emergency 

Resource Allocation on Expressway 

Chai Gan 

Transportation College Southeast University, Nanjing, 210096, China 

(+86)13851446229 

chai.gan@163.comSun Ying-ying 


(+86)13951613471 

sunyingying135@163.com 

Zhu Cang-hui 


(+86)13851900541 

zch211035@163.com 

ABSTRACT 

In order to allocate traffic emergency rescue resources on expressway, considering rescue time and resources costs as the objective, stochastic 

variables are introduced into constraints and a corresponding stochastic programming model is established, due to the stochastic resource requirements 

of accidents. Because of large numbers of rescue depots and black-spots, a stochastic simulation of particle swarm optimization (PSO) algorithm is put 

forward, and a particle presentation of Indirect Particle Position (IPP) is developed. By using the algorithm, the model need not to be converted into 

certain programming and is easy to solve. The model and the algorithm are used in the case of rescue resource allocation problem on expressway 

networks in Nanjing area, and the results show that the method is more effective and efficient than a traditional algorithm. In addition, the results can 

provide a reference for resource allocation of other expressway networks. 

Orthogonal Immune Algorithm with Diversity-based 

Selection for Numerical Optimization 

Maoguo Gong 


Key Lab of Intelligent Perception and 

Image Understanding of Ministry of 

Education of China, Institute of

Intelligent Information Processing, PO 

Box 224, Xidian University, Xi'an, 

710071, China 

+86-029-88202661 

gong@ieee.org 

Licheng Jiao 




Education of China, Institute of 



710071, China 

+86-029-88201023 

Lchjiao@mail.xidian.edu.cn 

Wenping Ma 




Education of China, Institute of 



710071, China 

+86-029-88202661 

wpma@mail.xidian.edu.cn 

ABSTRACT 

In this study, we design an Orthogonal Immune Algorithm (OIA) 

for numerical optimization by incorporating orthogonal 

initialization, a novel neighborhood orthogonal cloning operator, 

a static hypermutation operator, and a novel diversity-based 

selection operator. The OIA is unique in three respects: Firstly, a 

new selection method based on orthogonal arrays is provided in 

order to maintain diversity in the population. Secondly, the 

orthogonal design with quantization technique is introduced to 

generate initial population. Thirdly, the orthogonal design with 

the modified quantization technique is introduced into the cloning 

operator. In order to identify any improvement due to orthogonal 

initialization, diversity-based selection and neighborhood 

orthogonal cloning, we modify the OIA via replacing its 

orthogonal initialization by random initialization; replacing its 

diversity-based selection by a standard evolutionary operator 

(μ+λ)-selection operator; and replacing its neighborhood 

orthogonal cloning by proportional cloning, and compare the four 

version algorithms in solving eight benchmark functions and six 

composition functions. 

Large-scale Optimization Using Immune Algorithm 

Maoguo Gong 


Key Lab of Intelligent Perception 

and Image Understanding of Ministry 

of Education of China, 

Institute of Intelligent Information 

Processing, PO Box 224, 

Xidian University, 

Xi'an, 710071, China 

+86-029-88202661 

gong@ieee.org 

Licheng Jiao









+86-029-88201023 

Lchjiao@mail.xidian.edu.cn 

Wenping Ma 









+86-029-88202661 

wpma@mail.xidian.edu.cn 

ABSTRACT 

Immune-inspired optimization algorithms encoded the parameters 

into individuals where each individual represents a search point in 

the space of potential solutions. A large number of parameters 

would result in a large search space. Nowadays, there is little 

report about immune algorithms effectively solving numerical 

optimization problems with more than 100 parameters. In this 

paper, we introduce an improved immune algorithm, termed as 

Dual-Population Immune Algorithm (DPIA), to solve large-scale 

optimization problems. DPIA adopts two side-by-side 

populations, antibody population and memory population. The 

antibody population employs the cloning, affinity maturation, and 

selection operators, which emphasizes the global search. The 

memory population stores current representative antibodies and 

the update of the memory population pay more attention to 

maintain the population diversity. Normalized decimal-string 

representation makes DPIA more suitable for solving large-scale 

optimization problems. Special mutation and recombination 

methods are adopted to simulate the somatic mutation and 

receptor editing process. Experimental results on eight benchmark 

problems show that DPIA is effective to solve large-scale 

numerical optimization problems. 

A Bounded Diameter Minimum Spanning Tree Evolutionary 

Algorithm Based on Double Chromosome 

Fangqing Gu 

Faculty of Applied 

Mathematics 

Guangdong University of 

teachnology 

Guangdong Province,China 

gufangqing84@sina.com 

Hai-lin Liu 




teachnology


lhl@scnu.edu.cn 

Wei Liu 




teachnology 


liuwei-gdut@163.com 

ABSTRACT 

The Bounded Diameter Minimum Spanning Tree problem 

(BDMST) is a classical combinatorial optimization problem. 

In this paper,we propose a double chromosome evolutionary 

algorithm based on level coding and permutation coding for 

the BDMST problem. Double chromosome coding achieves 

the correspondences of the code and the solution of BDMST 

problem, so that the local search can be implemented more 

efficiently. A new crossover operator is design based on the 

double chromosome coding. The proposed algorithm keeps 

diversity and preferable convergence, because The offspring 

not only inherit the parent’s some sub-tree, but also generate 

some new edges. Designed a novel decoding strategy 

to the level code chromosome, might find the predecessor 

that associated with smaller costs. The proposed algorithm 

is empirically compared to edge-set coded genetic algorithm 

and a variable neighborhood search implementation on Euclidean 

instances based on complete graphs with up to 1000 

nodes considering either solution quality as well as computation 

time. It turns out that the evolutionary algorithm 

used double chromosome performs best the edge-set EA and 

the variable neighborhood search implementation concerning 

computation time. 

An Improved Quantum Genetic Algorithm for Stochastic 

Flexible Scheduling Problem with Breakdown 

Jinwei Gu 




86-21-64252576 

Cuiwen Cao 




86-21-64252576 


Bin Jiao 

Shanghai Dianji Univ. 

690 Jiangchuan Road 

Minhang District, Shanghai, China 

86-21-54758615 


Xingsheng Gu* 




86-21-64252576 

xsgu@ecust.edu.cn

ABSTRACT 

A stochastic flexible scheduling problem subject to random 

breakdowns is studied in this paper, which objective is to minimize 

the expected value of makespan. We consider a preemptive-resume 

model of breakdown. The processing times, breakdown intervals 

and repair times are random variables subjected to independent 

normal distributions. An expanding method inspired by paper [1] is 

devised through predicting expected breakdown time of machines. 

Based on some concepts of quantum evolution, an Improved 

Quantum Genetic Algorithm (IQGA) is proposed, which is tested on 

a sampling problem compared with Cooperative Co-evolutionary 

Genetic Algorithm (CCGA) and Genetic Algorithm (GA). 

Experiment results show IQGA has better feasibility and 

effectiveness. 

Categories and Subject Descriptors 

G.1.6 [Numerical Analysis]: Optimization – constrained 

optimization, global optimization, stochastic programming. 

General Terms: Algorithms. 

Keywords 

quantum algorithm; machine breakdown; stochastic scheduling 

Binary Particle Swarm Optimization Based Prediction 

of G-Protein-Coupled Receptor Families with Feature 

Selection 

Quan Gu 

College of Information Sciences and Technology, 

Donghua University 

Shanghai 201620, China 

Yongsheng Ding* 

College of Information Sciences and Technology, 


Engineering Research Center of Digitized Textile & 

Fashion Technology, Ministry of Education Shanghai 

201620, China 

+86 21 67792329 

*ysding@dhu.edu.cn 

ABSTRACT 

G-protein-coupled receptors (GPCRs), the largest family of 

membrane protein, play an important role in production of 

therapeutic drugs. The functions of GPCRs are closely correlated 

with their families. It is crucial to develop powerful tools to 

predict GPCRs families. In this study, Binary particle swarm 

optimization (BPSO) algorithm, which has a better optimization 

performance on discrete binary variables than particle swarm 

optimization (PSO), is applied to extract effective feature for 

amino acids pair compositions of GPCRs protein sequence. 

Ensemble classifier is used as prediction engine, of which the 

basic classifier is the fuzzy K-nearest neighbor (FKNN). Each 

basic classifier is trained with different feature sets. The results 

obtained by jackknife test are quite encouraging, indicating that 

the proposed method might become a potentially useful tool for 

GPCR prediction, or play a complimentary 

Classification of EEG Signals Using Relative Wavelet 

Energy and Artificial Neural Networks 

Ling Guo 

∗

Department of Information 

Technologies and 

Communications 

University of A Coruña, A 

Coruña, 15071, Spain 

lguo@udc.es 

Daniel Rivero 






drivero@udc.es 

Jose A.Seoane 






jseoane@udc.es 

Alejandro Pazos 






apazos@udc.es 

ABSTRACT 

Electroencephalographms (EEGs) are records of brain electrical 

activity. It is an indispensable tool for diagnosing neurological 

diseases, such as epilepsy. Wavelet transform (WT) 

is an effective tool for analysis of non-stationary signal, such 

as EEGs. Relative wavelet energy (RWE) provides information 

about the relative energy associated with different frequency 

bands present in EEG signals and their corresponding 

degree of importance. This paper deals with a novel 

method of analysis of EEG signals using relative wavelet 

energy, and classification using Artificial Neural Networks 

(ANNs). The obtained classification accuracy confirms that 

the proposed scheme has potential in classifying EEG signals. 

Path Planning Method for Robots in Complex Ground 

Environment Based on Cultural Algorithm 

Yi-nan Guo 

School of Information and Electronic 

Engineering, China University of 

Mining and Technology, Xuzhou,China 

86-516-83884749, 221116 

nanfly@126.com 

Mei Yang 




221116 

maynb@163.com

Jian Cheng 




221116 

fantastcj@126.com 

ABSTRACT 

In complex ground environment, different regions have different 

road conditions. Path planning for robots in such environment is an 

open problem, which lacks effective methods. A novel global path 

planning method based on common sense and evolution knowledge 

is proposed by adopting dual evolution structure in culture 

algorithms. Common sense describes ground information and 

feasibility of environment, which is used to evaluate and select the 

paths. Evolution knowledge describes the angle relationship 

between the path and the obstacles, or the common segments of 

paths, which is used to judge and repair infeasible individuals. 

Taken two types of environments with different obstacles and road 

conditions as examples, simulation results indicate that the 

algorithm can effectively solve path planning problem in complex 

ground environment and decrease the computation complexity for 

judgment and repair of infeasible individuals. It also can improve 

the convergence speed and have better computation stability. 

Cooperative Interactive Cultural Algorithms Adopting 

Knowledge Migration 

Yi-nan Guo 




86-516-83884749, 221116 

nanfly@126.com 

Jian Cheng 




221116 

fantastcj@126.com 

Yong Lin 




221116 

linyong@126.com 

ABSTRACT 

In many optimization problems with implicit indexes, human need 

to participate in the evaluation process synchronously in different 

computer nodes. And human is easy to feel tired. In order to 

alleviate human fatigue, implicit knowledge embodied in the 

evolution process, which reflect human cognition and preference, is 

extracted and utilized. However, how to effectively exchange 

information among nodes is not taken into account. Aiming at 

systemic analysis and effective application about implicit 

knowledge, cooperative interactive cultural algorithm adopting 

knowledge migration strategy is proposed. A novel knowledge 

model based on characteristic-vector is adopted to describe implicit 

knowledge embodied in the evolution process, including human 

cognitive tendency, the degree of human preference, the degree of 

human fatigue and human cognition schema. According to the

evolution status of population and human fatigue in each computer 

node, human cognition schemas are migrated between nodes. And 

common knowledge is obtained by coordination strategy and 

utilized to induce the evolution process of ICA in each computer 

node. Taking cooperative fashion design system as a testing 

platform, the rationality of knowledge migration strategy is proved. 

Simulation results indicate this algorithm can alleviate human 

fatigue and improve the speed of convergence effectively. 

Intrinsic Evolution of Digital Circuits Using Evolutionary 

Algorithms 

Guoliang He1,2, Yuanxiang Li1, Zhongzhi Shi2,Ting Hu3 

1State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China 

2Key Laboratory of Intelligent Information Process, Institute of Computing Technology, The Chinese Academy 

of Sciences, Beijing, China 

3Department of Computer Science, Memorial University of Newfoundland, St. John’s, Canada 

glhe@whu.edu.cn, yxli@whu.edu.cn, shizz@ics.ict.ac.cn, tingh@cs.mun.ca 

ABSTRACT 

Currently, the auto-design of electronic and analog circuits is an 

intensively studied topic in the field of evolvable hardware. In 

order to improve evolutionary design of logic circuits in 

efficiency, capability of optimization and safety of on-line 

evolution, an elitist pool evolutionary algorithm (EPEA) based on 

novel approaches is proposed. First, an extended matrix encoding 

method is devised, which can be able to reflect the potential 

performance of a circuit and reduce the risk of deleting a circuit 

with a good developing potential during evolution. Then, a novel 

sub-circuit crossover operator and an adaptive mutation strategy 

are introduced to improve the local optimization and maintain the 

diversity of a population in the evolution. At last, a framework of 

on-line evolution is used to implement EPEA on a fieldprogrammable 

gate array. Experiments show that our proposed 

method is able to design valid and novel circuits efficiently. 

Large Scale Function Optimization or High-Dimension 

Function Optimization in Large Using Simplex-based Genetic 

Algorithm 

ABSTRACT 

Xiao Hongfeng 

School of Information Science and Engineering, Central South University, China 8872564, 0731, 086 

xhf71@hunnu.edu.cn 

Tan Guanzheng 

School of Information Science and Engineering, Central South University, China 8876128, 0731, 086 

tgz@mail.csu.edu.cn 

Huang Jingui 

Department of Computer Education, Hunan Normal University, China 8872564, 0731, 086 

hjg@hunnu.edu.cn

Simplex genetic algorithm (Simplex-GA) is the fusion between the simplex multi-direction searches consisting in Nelder-Mead Simplex Method 

(NMSM), i.e., MDS-NMSM, and the evolutionary mechanism of genetic algorithm, i.e., selecting the superior and eliminating the inferior. One of 

important differences in evolution algorithms is that each evolution algorithm has its own especial reproduce operators. The reproduce operator of 

simplex-GA consists of an extremum mutation operator and directional reproduce operators. The extremum mutation operator is designed for the best 

individual, while the directional reproduce operators are devised for all individuals except the best individual and based on the multi-direction search 

of NMSM. The direction reproduce operators have four main features. (1)The first is that the directional reproduce operators are the combination of 

deterministic search and random search. (2)The second is that the directional reproduce operators search for new individuals according to a new mode 

from point-search, line-search to plane-search or solid-search; the point-search is a deterministic search, while line-search, plane-search and 

solid-search are random searches; deterministic search is prior to random search. (3)The third is that directional reproduce operators are embedded into 

multi-direction search of Nelder-Mead Simplex Method. Based on above three points, simplex is a primary element of simplex-GA. In this paper, we 

only discuss two extreme cases: low dimension simplex-GA (LD-Simplex-GA), where the dimensionality of simplex is small, and high dimension 

simplex-GA (HD-Simplex-GA), where the dimensionality of simplex is big. The elaborately selected eight test functions with 500-1500 dimensions 

are used to verify the performances of LD-simplex-GA and HD-Simplex-GA, and experiment results confirm that both LD-Simplex-GA and 

HD-Simplex-GA have the excellent capacity of optimizing the functions with large scale variants 

Model-based Compromise Control of Greenhouse Climate 

using Pareto Optimization ∗ 

Haigen Hu 

† 

Department of Control 

Science and Engineering 

Tongji University, 

Shanghai, China, 200092 

hnhhg@163.com 

Lihong Xu 

‡ 

Member,ACM 





xulhk@163.com 

Qingsong Hu 





hu.qingsong@163.com 

ABSTRACT 

Energy-saving is always in conflict with the control Errorminimizing 

for real-world engineering application in greenhouse. 

Moreover, the efficiency of plant production and 

energy consumption depends largely on the adjustment of 

greenhouse environment. In order to achieve less energy consumption 

and higher control precision, this paper presents a 

kind of compromise control algorithm for Pareto solutions of 

greenhouse environment control. The models of greenhouse 

and weather forecast used are described and derived. A series 

of optimization experiments are presented at any time 

of a day using Non-dominated Sorting Genetic Algorithm- 

II(NSGA-II). The results show the feasibility of the proposed 

method, and it may be valuable and helpful to formulate environmental 

control strategies, and to achieve high control 

precision and low energy cost. 

Non-even Spread NSGA-II and Its Application to 

Con_icting Multi-Objective Compatible Control ¤

Qingsong Huy 


Science and Engineering, 

Tongji University 

65# Chifeng Road 


huqs@ymail.com 

Lihong Xuz 




65# Chifeng Road 


xulhk@163.com 

Erik Goodman 

Department of Electrical and 

Computer, Michigan State 

University 

2340 Engineering College 

Building, MSU 

East Lansing, MI48824,USA 


ABSTRACT 

Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is 

a sound method to deal with the multi-objective optimiza- 

tion problem, and even spread Pareto front preserving strat- 

egy is one of its two key principles. However, especially for 

some dynamic problems, the most interested area is certain 

special area among the Pareto front. To meet this require- 

ment, the non-even Pareto front spread preserving princi- 

ple is proposed and is taken as the optimization tool for 

the multi-objective compatible control problem (MOCCP). 

To decrease the real-time computation load at every control 

step, based on the tight relation between the system states of 

the neighboring sampling instants, an iterative control algo- 

rithm is presented. The stability preference selection strat- 

egy in the algorithm tends to produce a stable controller 

in face of the Pareto front with the divergent or oscillating 

segment. To further decrease the computation time, adapt- 

able population corresponding with the control process is 

adopted. Comparative simulation example illustrates the 

validity. 

Guided Variable Neighborhood Harmony Search for Integrated 

Charge Planning in Primary Steelmaking Processes 

1, 2 

1, 2 

Min Huang , Hong-yu Dong , Xing-wei Wang 1 

, Bing-lin Zheng 2 

, W.H.Ip 3 

1 

College of Information Science and Engineering, Northeastern University Shenyang, 110004, China 

2 

Key Laboratory of Integrated Automation of Process Industry (Northeastern University), Ministry of Education, Shenyang, 

110004, China 

3 

Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong 

+86-24-83671469

mhuang@mail.neu.edu.cn 

hongyu.dong@gmail.com 

ABSTRACT 

The planning for rectangular plate products (slabs) in an integrated steel plant is extremely hard and important. Due to the large scale and complex 

integrated operational constraints, the planning problem is quite difficult to achieve an optimal solution even a feasible solution. From the practical 

point of view, this paper discusses an integrated charge planning (ICP) problem, with flexible product specifications. The purpose is to improve the 

efficiency and feasibility of planning, the customer satisfaction levels and the production costs, considering the integrated operational constraints. An 

integer programming model is formulated, and the problem is NP-hard. A new heuristics based on a variable neighborhood search (VNS), named the 

guided VNS embedded in harmony search, is designed. The computational results demonstrate that the proposed model and algorithm are feasible and 

effective for ICP. 

University Course Timetable Planning using Hybrid 

Particle Swarm Optimization 

Ho Sheau Fen @ Irene 

Faculty of Comp. Sc. & Info. Sys. 

Universiti Teknologi Malaysia 

81310. Johor, Malaysia 

+6019-8331338 

ireneluv@hotmail.com 

Deris Safaai 


Universiti Teknologi Malaysia 


+6019-7569202 

safaai@utm.my 

Mohd Hashim, Siti Zaiton 


Universiti Teknologi Malysia 


+6019-7726248 

sitizaiton@utm.my 

ABSTRACT 

University Course Timetabling (UCT) is a complex problem and 

cannot be dealt with using only a few general principles. The 

complicated relationships between time periods, subjects and 

classrooms make it difficult to obtain feasible solution. Thus, 

finding feasible solution for UCT is a continually challenging 

problem. This paper presents a hybrid particle swarm optimization 

algorithm to solve University Course Timetabling Problem 

(UCTP). The proposed approach (hybrid particle swarm 

optimization with constraint-based reasoning) uses particle swarm 

optimization to find the position of room and timeslot using 

suitable objective function and the constraints-based reasoning 

has been used to search for the best preference value based on the 

student capacity for each lesson in a reasonable computing time. 

The proposed algorithm has been validated with other hybrid 

algorithms (hybrid particle swarm optimization with local search 

and hybrid genetic algorithm with constraint-based reasoning) 

using a real world data from Faculty of Science at Ibb University 

– Yemen and results show that the proposed algorithm can 

provide more promising solution. 

The Impact of Network Topology on Self-Organizing Maps 

Fei J iang1, 2 , Hugues Berr y 1 , Marc Sc hoenauer2

1 Pr ojec t- Team Alc hemy, 

INRIA Saclay – Île-de-France, 

Par c O r s ay Univer s ité 

28, r ue J ean Ros tand 

91893 O r s ay Cedex , Franc e 

2Pr ojec t- Team TAO 

INRIA Sac lay – Île- de- Franc e & 

LRI ( UMR CNRS 8623) 

Bât 490, Univer s ité Par is - Sud 

91405 O r s ay Cedex , Franc e 

Fei. J iang@inr ia. f r, Hugues. Berr y @inr ia. f r, Marc . Sc hoenauer@inr ia. f r 

ABSTRACT 

In this paper, we study instances of complex neural net- 

works, i.e. neural networks with complex topologies. We use 

Self-Organizing Map neural networks whose neighborhood 

relationships are defined by a complex network, to classify 

handwritten digits. We show that topology has a small im- 

pact on performance and robustness to neuron failures, at 

least at long learning times. Performance may however be 

increased (by almost 10%) by evolutionary optimization of 

the network topology. In our experimental conditions, the 

evolved networks are more random than their parents, but 

display a more heterogeneous degree distribution. 

Comprehensive Analysis for Modified Particle Swarm 

Optimization with PD Controllers 

Jing Jie 

College of Software, 


of Technology1, Hangzhou City, 

310014; 

Taiyuan University of Science 

& Technology2, Taiyuan City, 030024 

13068085117, China, CN0086 

jjing277@sohu.com 

Jianchao Zeng 

Division of System Simulation 

& Computer Application, 

Taiyuan University of Science 

& Technology2, 

WaLiu Road 66#, Taiyuan City, 

030024 

0351-6998016, China, CN0086 


Wanliang Wang 

College of Software, 

Zhejiang University of 

Technology1, Hangzhou City, 

310014; 

0571-85290667,China, CN0086 

wwl@zjut.edu.cn 

ABSTRACT 

Inspired by the information prediction existing in the nature 

intelligent agents, the authors have developed a modified particle 

swarm optimization (PSO) with a forward PD controller (PSOFWPD) 

earlier. Comprehensive analysis for the model is provided 

in the paper, including its stabilization, convergence and dynamic 

behavior. Later, another modified PSO with a feedback PD

controller (PSO-FBPD) is presented companying some analysis. 

The introductions of different PD controllers develop the standard 

PSO(SPSO) with information prediction cabality, which can 

guide the particle to respond to the change of their exemplars 

correctly and rapidly, and greatly contributes to a successful 

global search. The proposed methods provide some new ideas for 

the improvement of SPSO, and are compared with SPSO based on 

some complex numerical optimization simulations. The relative 

experimental results show SPSO with different PD controller 

performs better than SPSO on the complex optimization 

problems. 

Combinatorial Effects of Local Structures and Scoring 

Metrics in Bayesian Optimization Algorithm 

Hossein Karshenas 

Iran University of Science 


Narmak, Tehran, Iran 

ho_karshenas 

@comp.iust.ac.ir 

Amin Nikanjam 




nikanjam@iust.ac.ir 

B. Hoda Helmi 




helmi@iust.ac.ir 

Adel T. Rahmani 




rahmani@iust.ac.ir 

ABSTRACT 

Bayesian Optimization Algorithm (BOA) has been used with 

different local structures to represent more complex models and a 

variety of scoring metrics to evaluate Bayesian network. But the 

combinatorial effects of these elements on the performance of 

BOA have not been investigated yet. In this paper the 

performance of BOA is studied using two criteria: Number of 

fitness evaluations and structural accuracy of the model. It is 

shown that simple exact local structures like CPT in conjunction 

with complexity penalizing BIC metric outperforms others in 

terms of model accuracy. But considering number of fitness 

evaluations (efficiency) of the algorithm, CPT with other 

complexity penalizing metric K2P performs better 

Hybrid Algorithms Based on Harmony Search and 

Differential Evolution for Global Optimization 

Ling-po Li 

Tsinghua National Laboratory for Information Science 

and Technology (TNList), Department of Automation,

Tsinghua University, Beijing, 100084, P.R. China 

llp03@mails.tsinghua.edu.cn 

Ling Wang 

Tsinghua National Laboratory for Information Science 

and Technology (TNList), Department of Automation, 

Tsinghua University, Beijing, 100084, P.R. China 

wangling@mail.tsinghua.edu.cn 

ABSTRACT 

In this paper, two hybrid algorithms are proposed for global 

optimization by merging the mechanisms of Harmony Search 

(HS) and Differential Evolution (DE). First, the learning 

mechanism of a variant of HS named Global-best Harmony 

Search (GHS) is embedded into the framework of DE to develop 

an algorithm called Global Harmony Differential Evolution 

(GHDE). Besides, the differential operator of DE is introduced 

into the framework of GHS to develop another new algorithm 

called Differential Harmony Search (DHS). Numerical 

simulations are carried out based a set of benchmarks. And 

simulation results and comparisons show that the hybrid 

algorithms are superior to the GHS and DE in terms of searching 

efficiency and searching quality. Meanwhile, the effect of some 

key parameters on the performances of DHS is investigated 

The Cloud-based Framework for Ant Colony Optimization 

Zhiyong Li 

School of Computer and 

Communication 

Hunan University, 410082 

Changsha, China 

jt_lizhiyong@hnu.cn 

Yong Wang 


Communication 



wangyong1179@gmail.com 

Kouassi K. S. Olivier 


Communication 



kouassy74@yahoo.fr 

Jun Chen 

Office Of Student Admission 



junc@hnu.cn 

Kenli Li 


Communication 



jt_lkl@hnu.cn 

ABSTRACT 

How to keep the balance between exploration in search space 

regions and exploitation of the search experience gathered so 

far is one of the most important issues in Ant Colony Optimization

(ACO). By using a variety of effective exploitation 

mechanisms and elite strategies, researchers proposed many 

sophisticated ACO algorithms, and obtains better results 

in experiments. In this paper, a new framework for implementing 

ACO algorithms called the cloud-based framework 

for ACO is proposed, which uses cloud model as the 

fuzzy membership function and constructs a self-adaptive 

mechanism with cloud model. By using the self-adaptive 

mechanism and the pheromone updating rule of suboptimal 

solutions which is determined by the membership function 

uncertainly, the cloud-based framework can make ACO algorithm 

explorer search space more effectively. Theoretical 

analysis on the cloud-based framework for ACO indicate 

that the framework is convergent, and the simulation results 

show that the framework can improve the ACO algorithms 

evidently. 

Multi-Objective Particle Swarm Optimization Algorithm Based 

on Game Strategies 

Zhiyong Li 


Communication, 

Hunan University 

Changsha, 10082, P.R. 

China. 


Songbing Liu 

Jun Chen 

Office of Student 

Admission 


Changsha, 410082, 

P.R. China 

junc@hnu.cn 

Kenli Li 




Changsha, 410082, P.R. 

China 

jt_lkl@hnu.cn 




Changsha,410082, P.R. 

China. 

liusongbing@gmail.com 

Degui Xiao 

School of Computer 

andCommunication, 


Changsha, 410082, P.R. China 

jt_dgxiao@hnu.cn

ABSTRACT 

Particle Swarm Optimization (PSO) is easier to realize and has a better performance than evolutionary algorithm in many fields. This 

paper proposes a novel multi-objective particle swarm optimization algorithm inspired from Game Strategies (GMOPSO), where 

those optimized objectives are looked as some independent agents which tend to optimize own objective function. Therefore, a multi- 

player game model is adopted into the multi-objective particle swarm algorithm, where appropriate game strategies could bring better 

multi-objective optimization performance. In the algorithm, novel bargain strategy among multiple agents and nondominated 

solutions archive method are designed for improving optimization performance. Moreover, the algorithm is validated by several 

simulation experiments and its performance is tested by different benchmark functions. 

Quantum Evolutionary Algorithm 

for Multi-Robot Coalition Formation 

Zhiyong Li 




410082 Changsha, 

Hunan, China 


Bo Xu 





Hunan, China 

xubo807127940@163.com 

Lei Yang 





Hunan, China 

jt_yl@hnu.cn 

Jun Chen 

Office Of Student Admission Of Hunan University 



Hunan, China 

junc@hnu.cn 

Kenli Li 

School of Computer and Communication, 



Hunan, China 

jt_lkl@hnu.cn 

ABSTRACT 

Coalition formation is an important cooperative method in 

Multi-Robot System, which has been paid more and more attention. 

However, efficient algorithm for multi-robot coalition is lack of 

various real-world applications in dynamic unknown environment. 

In such cases, the optimization algorithm has to track the changing 

optimum as close as possible, rather than just finding a static 

appropriate solution. In this paper, The Quantum Evolutionary 

Algorithm is proposed for solving this problem, where a skillful 

Quantum probability representation of chromosome coding strategy 

is designed to adapt to the complexity of the multi-robot coalition 

formation problem. Furthermore, a strategy for updating quantum

gate using the evolutionary equation is employed to avoid the 

premature convergence. Experiments results show that the proposed 

algorithm could solve the multi-robot coalition formation problem 

effectively and efficiently, and the proposed algorithm is valid and 

superior to other related methods as far as the stability and speed of 

convergence are concerned. 

Global Path Planning for Mobile Robot Based Genetic 

Algorithm and Modified Simulated Annealing Algorithm 

Yuming Liang, Lihong Xu 

School of Electronics and Information Engineering, Tongji University 

Room 304,ongji University science and technology garden 2nd building, 

No. 67 Chifeng road, Yangpu District, Shanghai 201804, China 

086-021-65980590 

{nc21.lym, xulhk}@163.com 

ABSTRACT 

Global path planning for mobile robot using genetic algorithm 

and simulated annealing algorithm is investigated in this paper. In 

view of the slow convergence speed of the conventional simulated 

annealing algorithm, a modified simulated annealing algorithm is 

presented, and a hybrid algorithm based on the modified 

simulated annealing algorithm and genetic algorithm is proposed. 

The proposed algorithm includes three steps: the MAKLINK 

graph theory is adopted to establish the free space model of 

mobile robots firstly, then Dijkstra algorithm is utilized for 

finding a feasible collision-free path and fixing on the sub-searchspace 

where the global optimal path inside, finally the global 

optimal path of mobile robots is obtained based on the hybrid 

algorithm of modified simulated annealing algorithm and genetic 

algorithm. Experimental results indicate that the proposed 

algorithm has better performance than simulated annealing 

algorithm and ant system algorithm in term of both solution 

quality and computational time, and thus it is a viable approach to 

mobile robot global path planning. 

Mobile Robot Global Path Planning Using Hybrid Modified 

Simulated Annealing Optimization Algorithm 

Yuming Liang, Lihong Xu 

School of Electronics and Information Engineering, Tongji University 

Room 304,o= Ongji University Science and Technology Garden 2nd building, 

No. 67 Chifeng road, Yangpu District, Shanghai 201804, China 

086-021-65980590 

{nc21.lym, xulhk}@163.com 

ABSTRACT 

Global path planning for mobile robot using simulated 

annealing algorithm is investigated in this paper. In view of the 

slow convergence speed of the conventional simulated annealing 

algorithm, a modified simulated annealing algorithm is presented, 

and a hybrid algorithm based on the modified simulated annealing 

algorithm and conjugate direction method is proposed. On each 

temperature, conjugate direction method is utilized for searching 

local optimal solution firstly, then the modified simulated 

annealing algorithm is employed to move off local optimal 

solution, and then the temperature is updated; these operations are 

repeated until a termination criterion is satisfied. Experimental 

results indicate that the proposed algorithm has better 

performance than simulated annealing algorithm and conjugate 

direction method in term of both solution quality and

computational time, and thus it is a viable approach to mobile 

robot global path planning. 

An Immune Algorithm for Complex Fuzzy Cognitive Map 

Partitioning 

Lin Chunmei 

Department of Computer 

Shaoxing College of Arts and 

Sciences Shaoxing Zhejiang 312000 

CHINA 

086-0575-88321046 

lin@mail.dhu.edu.cn 

ABSTRACT 

Fuzzy cognitive map is an approach to knowledge representation 

and inference that are essential to any intelligent system; it 

emphasizes the connections of concepts as basic units for storing 

knowledge, and the structure represents the significance of 

system. It can be used for designing knowledge base, modeling 

and controlling complex systems. However, modern systems 

are characterized as complex systems with high dimension and a 

variety of variables and factors, when a large of nodes is included 

and the cause relation among concept-nodes is complex in the 

system, the inference, verification and maintenance of knowledge 

are very difficult. In this paper, we first analyze the knowledge 

representation and the inference mechanism of fuzzy cognitive 

map. Further, we present to partition the complex fuzzy cognitive 

map base into smaller chunks based on immune algorithm. In the 

methodology, we utilize the feature of fuzzy cognitive map to 

construct partition rules and criticize rules. Finally, an illustrative 

example is provided, and its results suggest that the method is 

capable of partitioning fuzzy cognitive map. 

Parameters Optimization on Dent around Fuel Filler 

of Auto Rear Fender Based on Intelligent Algorithm 

Jianping Lin 

School of Mechanical 

Engineering, 


NO.4800, Caoan Road, 


+8613901719457 

jplin58@tongji.edu.cn 

Shuisheng Chen 


Engineering, 




+8613501941311 

9wlchen@tongji.edu.cn 

Ying Cao 


Engineering, 




+8613601905310

lemoncaoy@163.com 

Huajun Guan 

Press Center, Shanghai 

Volkswagen Automotive 

Co., Ltd. 

NO.5288 CaoAn Road, 

Anting, Shanghai, China 

+862169564609 

guanhuaj@csvw.com 

ABSTRACT 

Dent is one of crucial surface defects in sheet metal forming. To 

improve the cosmetic surface qualities, it is important to optimize 

the process parameters to avoid dent defects in forming parts and 

to minimize production cost. The relationships between defects 

magnitude and forming parameters like die radius, punch radius, 

fuel filler radius, blank holder force (BHF) and friction coefficient 

can be established through finite element analysis (FEA). A 

reduced set of finite element simulations are carried out as per the 

orthogonal design array. Take the depth z and the width L of 

the surface dent as optimization objectives, an optimization 

methodology based on the use of orthogonal design method and 

the response surface technique based on Feedforward Neural 

Networks (FNN) is proposed to obtain the optimum values of 

above forming parameters, which can reduce the dent without 

cracking and damage of product, and z and L is gained 

depending on the optimized parameters by FEA. The optimization 

results of parameters are compared with the ones achieved by 

Trial and Error approach in industry. The result indicates that the 

proposed method is efficient for surface dents controlling. 

Parameters Optimization of Support Vector Regression 

Based on Immune Particle Swarm Optimization Algorithm 

Yan Wang, Juexin Wang, Wei Du, Chen Zhang, Yu Zhang, Chunguang Zhou 

College of Computer Science and Technology, Jilin University 

Changchun 130012, China 

wy6868@hotmail.com; cgzhou@jlu.edu.cn 

ABSTRACT 

A novel Immune Particle Swarm Optimization (IPSO) for parameters 

optimization of Support Vector Regression (SVR) is proposed 

in this article. After introduced clonal copy and mutation process 

of Immune Algorithm (IA), the particle of PSO is considered as 

antibodies. Therefore, evaluated the fitness of particles by the 

Leave-One-Out Cross-Validation (LOOCV) standard, the best 

individual mutated particle for each cloned group will be selected 

to compose the next generation to get better parameters of SVR. It 

can construct high accuracy and generalization performance regression 

model rapidly by optimizing the combination of three 

SVR parameters at the same time. Under the datasets generated 

from sinx function with additive noise and spectra dataset, simulation 

results show that the new method can determine the parameters 

of SVR quickly and the gotten models have superior learning 

accuracy and generalization performance. 

Estimation of Distribution Algorithm Based

on Archimedean Copulas 

Li-Fang Wang1,2 

wlf1001@163.com 

Jian-Chao Zeng2 


Yi Hong1 

yudongmei@cnn.cn 

1. College of Electrical and Information Engineering, 

Lanzhou University of Technology, Lanzhou, 730050, 

China 

2. Complex System and Computational Intelligence 

Laboratory, Taiyuan University of Science and 

Technology, Taiyuan, 030024, China 

ABSTRACT 

Both Estimation of Distribution Algorithms (EDAs) and Copula 

Theory are hot topics in different research domains. The key of 

EDAs is modeling and sampling the probability distribution 

function which need much time in the available algorithms. 

Moreover, the modeled probability distribution function can not 

reflect the correct relationship between variables of the 

optimization target. Copula Theory provides a correlation between 

univariable marginal distribution functions and the joint 

probability distribution function. Therefore, Copula Theory could 

be used in EDAs. Because Archimedean copulas possess many 

nice properties, an EDA based on Archimedean copulas is 

presented in this paper. The experimental results show the 

effectiveness of the proposed algorithm. 

Categories and Subject Descriptors 

Case Study of Finite Resource Optimization in FPGA 

Using Genetic Algorithm 

JingXia Wang 

Department of Electrical Engineering 

Shenzhen Polytechnic, XiLi Lake, NanShan 

District, ShenZhen, GuangDong, 518055, P.R.C 

86-755-26731243 

ljwjxlyr2005@yahoo.com 

Sin Ming Loo 

Department of Electrical and Computer Engineering 

Boise State University, 1910 University Drive 

Boise, ID 83725, USA 

1-208-426-5679 

smloo@boisestate.edu 

ABSTRACT 

Modern Field-Programmable Gate Arrays (FPGAs) are becoming 

very popular in embedded systems and high-performance 

applications. FPGA has benefited from the shrinking of transistor 

feature size, which allows more on-chip reconfigurable (e.g. 

memories and look-up tables) and routing resources. 

Unfortunately, the amount of reconfigurable resources in a FPGA 

is fixed and limited. This paper investigates an applicationmapping 

scheme in FPGA by utilizing sequential processing units 

and task specific hardware. Genetic Algorithm is used in this 

study. We found that placing sequential processor cores into 

FPGA can improve the resource utilization efficiency and

achieved acceptable system performance. In this paper, two cases 

were studied to determine the trade-off between resource 

optimization and system performance. 

DynamicTrust: Three-Dimensional Dynamic Computing 

Model of Trust in Peer-to-Peer Networks 

Fengming Liu 

School of Management and 

Economics, 

Shandong Normal University 

No.88 Wenhua Road(E.), Jinnan 

Shandong Province, P.R. China 

86+531+86180509 

liufm69@163.com 

Wenyin Zhang 

Information School, 

Linyi Normal University 

Linyi, Shandong, P.R.China 

86+539-2060257 

zwyxrx@163.com 

Yongsheng Ding 

College of Information Sciences 

and Technology, 


No. 2999, Renmin Road (N.) 

Songjiang, Shanghai P.R. China 

86+21+67792323 

ysding@dhu.edu.cn 

Xiyu Liu 


Economics, 




86+531+86180509 

sdxyliu@163.com 

Mingchun Zheng 


Economics, 




86+531+86180509 

zhmc163@163.com 

Yu Liu 

Department of commerce, 

Jinan Technology College 

No.48 Maanshan Road(E.), Jinnan 


86+531+86301137 

Mrliu73@126.com 

ABSTRACT 

With the application of peer-to-peer network, how to promote 

cooperation between peers has gotten more and more important. 

Most of traditional security technologies can not be applied in 

P2P network very well to promote the cooperation because of the

special characteristics of P2P network such as openness and 

anonymity, etc. Trust has been proven to be essential to enforcing 

cooperative behavior in peer-to-peer networks. Trust relationship 

depends on trustee’s trustworthiness. So, in this paper, we present 

a three-dimensional computing model of dynamic trust to try to 

find a way to address the problem. Firstly, we give a threedimensional 

computing model of trust and make a dynamics 

analysis to trust of peer. Next, considered the new peer without 

trustworthiness can not do anything almost, we propose an 

algorithm of initial trustworthiness based on the new peer’s 

abilities. To compute the direct trustworthiness and the 

recommended trustworthiness, we colligate the time as a dynamic 

factor. Finally, based on the trustworthiness computed by trust 

fusion algorithm, we present a mechanism of making trust 

decision to promote cooperation. The simulation results have 

showed that our model can enhance the cooperation between 

peers and avoid the malicious peers from destroying behaviors. 

SRDE: An Improved Differential Evolution Based 

on Stochastic Ranking 

Jinchao Liu 

Technical University of Denmark 

Nils Koppels Alle 

Kgs. Lyngby 2800, Denmark 

+45 4525 5602 

jliu@man.dtu.dk 

Zhun Fan 

Technical University of Denmark 

Nils Koppels Alle 

Kgs. Lyngby 2800, Denmark 

+45 4525 6271 

zf@mek.dtu.dk 

Erik Goodman 

2120 Engineering Building, MSU 

East Lansing, MI, 48824, USA 

+01 517 355 6453 


ABSTRACT 

In this paper, we propose a methodology to improve the 

performance of the standard Differential Evolution (DE) in 

constraint optimization applications, in terms of accelerating its 

search speed, and improving the success rate. One critical 

mechanism embedded in the approach is applying Stochastic 

Ranking (SR) to rank the whole population of individuals with 

both objective value and constraint violation to be compared. The 

ranked population is then in a better shape to provide useful 

information e.g. direction to guide the search process. The 

performance of the proposed approach, which we call SRDE 

(Stochastic Ranking based Differential Evolution) is investigated 

and compared with standard DE with two variants of mutation 

strategies. The experimental results show that SRDE outperforms, 

or at least is comparable with standard DE in both variants in all 

the tested benchmark functions.

An Exploratory Study on Dominance Resistant Solutions 

in Many Objectives Optimization 

Liu Liu 

School of Management 

Minqiang Li 

School of Management 

Tianjin University, Tianjin, 300072 

{liuliu, mqli, dlin}@tju.edu.cn 

Lin Dan 

School of Science 

ABSTRACT 

In spite of many approaches have been proposed to improve the 

performance of evolutionary multiobjective algorithms on many 

objectives optimization, little work was to explore the essential 

reason that the algorithms, such as the NSGA-II and SPEA2, 

deteriorates distinctly with increased number of objectives. One 

of the popular explanations is that the high proportion of 

nondominated solutions in the population breaks down the 

search of the algorithms. However, this paper attempts to 

explain that some dominance resistant solutions (DRSs) (except 

the extreme individuals which are located on coordinate axis 

greater than the extreme points of the true Pareto front), which 

are hard to be dominated and far away from the true Pareto front, 

constitute the essential handicap for the population evolution. It 

is observed this kind of solution is generated at the beginning of 

the evolution, and further delays the convergence of the 

algorithms due to diversity promoting methods. We make an 

analytical explanation of the four representative approaches that 

were originally proposed to address many objectives problems. 

At last, under a new framework of MOEA evolved with only 

nondominated solutions, our experimental results verify their 

performance on DTLZ1 and DTLZ2 problems with 4, 5, 6 

objectives respectively. 

Designing Fair Flow Fuzzy Controller Using Genetic 

Algorithm for Computer Networks 

Weirong Liu, Min Wu, Jun Peng and Guojun Wang 

School of Information Science and Engineering 

Central South University 

Changsha 410083, China 

Weirong_liu@126.com 

ABSTRACT 

To utilize the link bandwidth efficiently in network, F.P.Kelly 

proposed the classic optimal model using utility function, which 

can converge to proportional fair point with asymptotic stability. 

However, the primal algorithm of Kelly model leads to the packet 

accumulation in the queue of the bottleneck link. By using 

heuristic fuzzy rules, this paper designs a fuzzy controller to 

adjust the additive increase parameter of the primal algorithm 

dynamically. Then genetic algorithm is used to optimize the 

scaling gains of the fuzzy controller, which is called GA-based 

fuzzy controller in this paper. The primal algorithm with the GAbased 

fuzzy controller can avoid the packet accumulation and 

keep the fairness and asymptotical stability. Thus it improves the 

performance of the primal algorithm.

Decision of Optimal Scheduling Scheme for Gas Field 

Pipeline Network Based on Hybrid Genetic Algorithm 

Wu Liu, Min Li 

School of Petroleum Engineering 

Southwest Petroleum University 

Chengdu, China 

+86-28-83033834 

wwwww65@126.com 

Yi Liu 

Xi’an Changqing Technology 

Engineering Co. LTD. 


+86-29-86599299 

liuandy@sina.com 

Yuan Xu, Xinglan Yang 

Graduate School 

Southwest Petroleum University 

Chengdu, China 

+86-28-83032146 

xyswpi@163.com 

ABSTRACT 

A mathematical model of optimal scheduling scheme for natural 

gas pipeline network is established, which takes minimal annual 

operating cost of compressor stations as objective function after 

comprehensively considering the resources of gas field, operating 

parameters of compressor stations and work conditions of pipeline 

system. In the light of the characteristics of the objective function 

such as nonliner, more optimal variables and complicated 

constraint conditions, based on the thought of modern heuristic 

evolutionary-algorithm, this paper presented a new hybrid genetic 

algorithm, which is featured by global search, fast convergence 

and strong robustness. It combined the reproduction strategy of 

differential evolution algorithm with the crossover and mutation 

of genetic algorithm. With the dynamic calibration of fitness and 

the elitism strategy of the optimal individual, this algorithm can 

improve the ability of searching and avoid the premature 

convergence effectively. The case study of a certain pipeline 

network system with 11 nodes, 11 pipelines,2 compressor stations 

demonstrates the effectiveness and application of the established 

model and algorithm. The optimal scheduling scheme could be 

adapted to daily operation and future retrofit of gas pipeline 

network. 

The Design of Three-motor Intelligent Synchronous 

Decoupling Control System 

Xingqiao Liu 

School of Electrical and 

Information Engineering 

Jiangsu University, Zhenjiang, 

China 

051188780895+86 

xqliu@ujs.edu.cn 

Jianqun Hu 


Information Engineering


China 

051188972520+86 

zjqyhjq@163.com 

Shaoqing Teng 


Information Engineering 


China 

051188972520+86 

tengshaoqing@126.com 

Liang Zhao 

School of Electrical and Information Engineering 

Jiangsu University, Zhenjiang, China 

051188972041+86 

tczhaoliang2006@126.com 

Guohai Liu 

School of Electrical and Information Engineering 

Jiangsu University, Zhenjiang, China 

051188780821+86 

ghliu@ujs.edu.cn 

ABSTRACT 

Aiming at the characteristics of multi-input and multi-output, 

nonlinearity, time-variation and strong coupling in the threemotor 

synchronous control system, and on the basis of 

mathematic model analysis of three-motor synchronous control 

system, the neural network control system is designed. It is 

composed of three intelligent PID controllers based on BP neural 

network arithmetic which adjusts the parameters of PID 

controllers on-line and neuron decoupling compensator. The 

control of speed and tension of system is realized by three 

intelligent PID controllers based on BP neural network, and the 

decoupling control of coupled variables is achieved by neuron 

decoupling compensator. Experiment is combined with PLC, and 

the results indicate that the control system can get some optimal 

parameters of the PID controllers according to different running 

state of system. The method is designed to realize better 

decoupling control between the speed and tension in the system, 

and it has better dynamic and static characteristics. 

A Simulated Annealing Algorithm with a new 

Neighborhood Structure for the Timetabling Problem 

Yongkai Liu 

Department of Computer Science, 

Xiamen University 

Xiamen, 361005, China 

+86 

yongkailiu@sina.com 

Defu Zhang 

Department of Computer Science, 


Xiamen, 361005, China 

+86 592 5918207 

dfzhang@xmu.edu.cn 

Stephen C.H. Leung 

Department of Management Sciences, 

City University of Hong Kong, 83 Tat

Chee Avenue, Kowloon, Hong Kong 

mssleung@cityu.edu.hk 

ABSTRACT 

In this paper, a new neighborhood structure is presented. The new 

neighborhood is obtained by performing a sequence of swaps 

between two timeslots, instead of only one move in the standard 

neighborhood structure. Based on new neighborhood, simulated 

annealing algorithm can solve the timetabling problem well. The 

computation results on two open benchmarks coming from two 

real-world high schools timetabling problems prove that the 

simulated annealing algorithm based on new neighborhood can 

compete with other effective approaches. 

Multi-swarm Particle Swarm Optimization Based Risk 

Management Model for Virtual Enterprise 

Fu-Qiang Lu 1,2 

, Min Huang 1,2 

, Wai-Ki Ching 3 

, Xing-Wei Wang 1,2 

, Xian-li Sun 1,2 

1 College of Information Science and Engineering, Northeastern University 

2 Key Laboratory of Integrated Automation of Process Industry (Northeastern University), Ministry of Education 

3 Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong 

+86-2483671469, Shenyang, China, 110004 

fuqiang_lu@126.com 

ABSTRACT 

Virtual Enterprise (VE) is a main scheme of enterprises in the 21st century. There are various risks for VE, due to VE’s agility and diversity of its 

members and its distributed characteristics. This paper presents a novel risk management model for VE, a Constructional Distributed Decision 

Making (CDDM) model. The model has two levels, namely, the top-model and the base-model, which describe the decision processes of the owner 

and the partners respectively. In this model, the situation of information symmetry between owner and partners is considered. The size of the search 

space will be very huge, when the number of members in VE, the number of risk factors and the number of actions increase. In addition, there are 

multiple members in VE. Considering the biological and computational motivations, a Multi-swarm Particle Swarm Optimization (MPSO) is then 

designed to solve the resulting optimization problem. Simulation results show the effectiveness of the proposed algorithm. 

A Collaborative Optimized Genetic Algorithm Based 

on Regulation Mechanism of Neuroendocrine-Immune 

System 

Bao Liu 

Information and Control 

Engineering College, 

China University of Petroleum 

Dongying, 257061, P.R. 

lb314423@163.com 

Yongsheng Ding1,2 

1 College of Information Sciences 

and Technology, Donghua University 

2 Engineering Research Center of 

Digitized Textile & Fashion 

Technology, Ministry of Education,

Shanghai 201620, P.R. China 


Jun-Hong Wang 

Information and Control 

Engineering College, 

China University of Petroleum 

Dongying, 257061, P.R. China 

wjhql@sohu.com 

ABSTRACT 

In this paper, an improved collaborative optimized genetic 

algorithm (CGA) inspired from the modulation mechanism of 

neuroendocrine-immune system is presented. The CGA has 

several features as follows. The first is that the parent individuals 

are not involved in the copy process. The second is that more 

excellent individuals may be produced due to the adaptive 

crossover and variation probability based on the hormone 

modulation. In order to examine its performance, firstly, two 

typical test functions are selected as the simulation models. Then 

CGA is applied to an intelligent controller based on the 

modulation of epinephrine (EIC). The simulation results show that 

the CGA has quicker convergence rate and higher searching 

precision than that of immune genetic algorithm and normal 

genetic algorithm, 

A Discrete Particle Swarm Optimization Algorithm 

with Fully Communicated Information 

Lu Qiang 

School of Automation 

Hangzhou Dianzi University 

Hangzhou, China 

lvqiang@hdu.edu.cn 

Qiu Xue-na 

School of Telecommunication 

Ningbo University of Technology 

Ningbo, China 

qiuxn26@hotmail.com 

Liu Shi-rong 



Hangzhou,China 

liushirong@hdu.edu.cn 

ABSTRACT 

In this paper, a novel discrete particle swarm optimization 

(DPSO) algorithm is presented for solving the combinational 

optimization problems such as knapsack and clustering. The 

proposed algorithm mainly employs the idea of the information 

stored and exchanged among particles through 

Information-Shared Matrix (ISM). There are two reasons for 

using the idea. To begin with, the mechanism, storing and 

exchanging information, makes it possible to construct a discrete 

algorithm to solve combinational problems. Furthermore, the 

positions of particles in the space are adjusted according to not 

only historical information and global information current 

particles left, but also the information the other particles left. 

Therefore, information can be more sufficiently shared by each 

particle. The performance of DPSO algorithm is evaluated in 

comparison with well-known ACO algorithm, TS algorithm and 

other discrete PSO algorithms. Our computational simulations 

reveal very encouraging results in terms of the quality of solution

found. 

Face Recognition Using Immune Network Based 

on Principal Component Analysis 

Guan-Chun Luh 

Tatung University 

No. 40, Sec. 3, Jhongshan N. Rd., Taipei City, 

Taiwan, ROC 

886-2-25925252 Ext. 3410 Re-Ext. 806 

gluh@ttu.edu.tw 

Ching-Chou Hsieh 

Tatung University 

No. 40, Sec. 3, Jhongshan N. Rd., Taipei City, 

Taiwan, ROC 

886-2-25925252 Ext. 3410 Re-Ext. 804 

kikidro@yahoo.com.tw 

ABSTRACT 

This paper proposes a face recognition method using artificial 

immune network classifiers based on Principal Component 

Analysis (PCA). The PCA abstracts principal eigenvectors of the 

image in order to get best feature description, hence to reduce the 

number of inputs of immune networks. After this, these image 

data of reduced dimensions are input into an immune network to 

be trained. Subsequently the antibodies of the immune networks 

are optimized using genetic algorithms. The performance of the 

present method was evaluated using the AT&T Laboratories 

Cambridge database (formerly called ORL face database). The 

results show that this method gains higher recognition rate in 

contrast with some other methods. 

Kernel-based Immunity Synergetic Network 

for Image Classification 

Xiuli Ma 

School of Communication and 

Information Engineering, 

Shanghai University, 

No.149 Yanchang Road, 


+86 21 5633 1619 

xlma@shu.edu.cn 

Guoqiang Mu 

Delphi China Technical Research 

Center, 

No.118 Delin Road, 


+86 21 2896 7503 

becky.mu@delphi.com 

Xiaoqing Yu 






+86 21 5633 1619

yxq@staff.shu.edu.cn 

ABSTRACT 

In order to reduce the relativity among prototype pattern vectors 

and to enhance the separability among different patterns, a novel 

kernel-based learning algorithm of Synergetic Neural Network 

(SNN) is proposed. The algorithm first maps the data from 

original space into a new feature space and then classifies them by 

a two-layered SNN. An optimization method of weighted factors 

in the two-layered SNN is also presented. It gives different 

patterns to different weights and makes full use of the global and 

local searching ability of Immunity Clonal Algorithm (ICA). 

Experiments on Iris dataset, textural images and Synthetic 

Aperture Radar (SAR) images show that the new algorithm does 

not only improve the classification rate but also has shorter 

training and testing time. 

Spectral Clustering Ensemble for Image Segmentation 

Xiuli Ma 






+86 21 5633 1619 

xlma@shu.edu.cn 

Wanggen Wan 






+86 21 5633 4945 

wanwg@staff.shu.edu.cn 

Licheng Jiao 


Processing, 


No.2 South Taibai Road, 

Xi’an 710071, China 

+86 29 8820 2234 

lchjiao@mail.xidian.edu.cn 

ABSTRACT 

To make full use of information included in a dataset, a multiway 

spectral clustering algorithm with joint model is applied to image 

segmentation. To overcome the sensitivity of the joint modelbased 

multiway spectral clustering to kernel parameter and to 

produce the robust and stable segmentation results, spectral 

clustering ensemble algorithm is proposed in this paper, which 

can make full use of the built-in randomness of spectral clustering 

and the inaccuracy of Nystrom approximation to produce 

diversity. Experiments on UCI dataset, textural and SAR images 

show that, after cluster ensemble, the new algorithm is not only 

more robust but also better quality. Therefore, the new algorithm 

is effective 

Fuzzy CMAC with Automatic State Partition for 

Reinforcement Learning

Huaqing Min 

South China University of 

Technology 

hqmin@scut.edu.cn 

Jiaan Zeng 


Technology 

an_ronaldor@yahoo.com.cn 

Ronghua Luo 


Technology 

rhluo@scut.edu.cn 

ABSTRACT 

Most of reinforcement learning (RL) algorithms use value 

function to seek the optimal policy. In large or even con- 

tinuous states, function approximation approaches must be 

used to represent value function. The structures of function 

approximators influence the learning performance greatly. 

However, the design of structures relies too much on human 

designer and inappropriate design can lead to poor perfor- 

mance. In this paper, we propose a novel function approxi- 

mator called Fuzzy CMAC (FCMAC) with automatic state 

partition (ASP-FCMAC) to automate the structure design 

for FCMAC. Based on CMAC (also known as tile coding), 

ASP-FCMAC employs fuzzy membership function to lower 

the computation load, and analyzes Bellman error as well as 

learning weights to partition the state automatically so as to 

generate the structure of FCMAC. Empirical results in both 

mountain car and RoboCup Keepaway domains demonstrate 

that ASP-FCMAC can automatically generate the structure 

of FCMAC and agent using it can learn efficiently. 

SO-Antnet for Improving Load Sharing in MANET 

Joseph C. Mushi Guanzheng Tan 

Central South University Central South University 

Changsha 410083, P. R. China Changsha 410083, P. R. China 

mushyjc@yahoo.co.uk tgz@mail.csu.edu.cn 

ABSTRACT 

SO-Antnet introduces new idea of load balancing over mobile adhoc 

networks based on intelligent agents inspired by organic 

metaphor of ants’ food foraging behavior. With inspiration from 

Antnet approach, this study improves theoretical derivation of 

objective function by consider contribution of all four 

characteristics of ants’ foraging behavior to achieve Self- 

Organization of a system. The study uses this objective function 

to optimize operation of intelligent agents, which collect 

information in mobile ad-hoc networks, to help the node to 

optimize route-cache contents and means of finding optimal path 

to particular destination. The study implements operational 

behavior of SO-Antnet by customizes DSR routing protocol 

modules in network simulator NS2. One major difference with 

other related work is that SO-Antnet simulation considers really 

cache implementation. Simulation results are compared with DSR 

performance, which show improvement in load balancing.

Virus-Evolutionary Genetic Algorithm Based Selective 

Ensemble Classifier for Pedestrian Detection 

B. Ning1,2, X.B. Cao1,2 , Y.W. Xu1,2, J. Zhang3 

1Department of Computer Science and Technology, University of Science and Technology of China, Hefei, 230026, 

P.R.China 

2Anhui Province Key Laboratory of Software in Computing and Communication, Hefei, 230026, P.R.China 

3School of Electronic and Information Engineering, Beihang University, Beijing, 100083, P.R.China 

ningbo@mail.ustc.edu.cn,xbcao@ustc.edu.cn,ywxu@mail.ustc.edu.cn,buaazhangjun@vip.sina.com 

ABSTRACT 

In pedestrian detection system, it is critical to determine whether 

a candidate region contains a pedestrian both quickly and reliably. 

Therefore, an efficient classifier must be designed. In general, a 

well-organized assembly classifier outperforms than single 

classifiers. For pedestrian detection, due to the complexity of 

scene and vast number of candidate regions, an efficient ensemble 

method is needed. 

In this paper, we propose a virus evolutionary genetic algorithm 

(VEGA) based selective ensemble classifier for pedestrian 

detection system, in which only part of the trained learners are 

selected and participate the majority voting for the detection. 

Component learners are trained with diversity and then VEGA is 

employed to optimize the selection of component learners. 

Moreover, a time-spending factor is added to the fitness function 

so as to balance the detection rate and detection speed. 

Experiments show that, comparing with typical non-selective 

Bagging and GA-based selective ensemble method, the VEGAbased 

selective ensemble gets better performance not only in 

detecting accuracy but also in detection speed. 

Hierarchical Oriented Genetic Algorithms for Coverage 

Path Planning of Multi-robot Teams with Load Balancing 

Metin Ozkan1 Ahmet Yazici1 Muzaffer Kapanoglu2 Osman Parlaktuna3 

meozkan@ogu.edu.tr, ayazici@ogu.edu.tr, muzaffer@ogu.edu.tr, oparlak@ogu.edu.tr 

1,2,3Eskisehir Osmangazi University 

+90 222 2393750 

1Department of Computer Eng., 2Department of Industrial Eng., 3Department of Electrical Eng., 

Batimeselik, 26480, Eskisehir, Turkey 

ABSTRACT 

Multi-robot coverage path planning problems require every point 

in a given area to be covered by at least one member of the robot 

team using their sensors. For a time-efficient coverage, the 

environment needs to be partitioned among robots in a balanced 

manner. So the problem can be modeled as task assignment 

problem with load balancing. In this study, we propose two 

oriented genetic algorithms working in a hierarchical manner to 

deal with this problem. In the first phase, a previously proposed 

oriented genetic algorithm is used to find a single route with 

minimum repeated coverage. In the following phase, a directed 

genetic algorithm is used to partition the route among robots 

considering load balancing. The algorithm is coded in C++ and 

simulations are conducted using P3-DX mobile robots in the 

MobileSim environment. The hierarchical oriented genetic 

algorithm (HOGA) is also compared to the multi-robot spanning 

tree coverage (STC) approach in terms of load balancing. The 

comparison indicates competitive results over multi-robot STC. 

A Multi-Objective-Based Non-Stationary UAV Assignment

Model for Constraints Handling using PSO 

Feng Pan 

Department of Automatic Control 

Beijing Institute of Technology 

Beijing, 100081, P.R.China 

+86-10-68948971 

andropan@gmail.com 

Guanghui Wang 




+86-10-68948971 

wangguanghui0927@163.com 

Yang Liu 




+86-10-68948971 

liuyang618@gmail.com 

ABSTRACT 

An unmanned aerial vehicle (UAV) assignment requires allocating 

vehicles to destinations to complete various jobs. It is a complex 

assignment problem with hard constraints, and potential 

dimensional explosion when the scenarios become more 

complicated and the size of problems increases. Moreover, the 

non-stationary UAV assignment problem, studied in the paper, is 

more difficult, since dynamic scenarios are introduced, e.g. 

change of the number, or different task requirements of targets and 

vehicle, etc. In this paper, a "Constraint-First-Objective- Next" 

model is proposed for the non-stationary problem. The proposed 

model can effectively handle constraints as an additional objective, 

including constraints expressed by nature language, and is flexible 

enough to be combined with kinds of intelligent computation 

algorithms. A local version of PSO is cooperated with the 

proposed model to solve non-stationary UAV assignment 

problems. Numerical experimental results illustrate that it can 

efficiently achieve the optima and demonstrate the effectiveness. 

Cooperative Micro–Particle Swarm Optimization 

Konstantinos E. Parsopoulos 

Department of Mathematics 

University of Patras 

GR–26110 Patras, Greece 

kostasp@math.upatras.gr 

ABSTRACT 

Cooperative approaches have proved to be very useful in evolutionary 

computation due to their ability to solve efficiently 

high-dimensional complex problems through the cooperation 

of low–dimensional subpopulations. On the other hand, 

Micro–evolutionary approaches employ very small populations 

of just a few individuals to provide solutions rapidly. 

However, the small population size renders them prone to 

search stagnation. This paper introduces Cooperative Micro– 

Particle Swarm Optimization, which employs cooperative 

low–dimensional and low–cardinality subswarms to concurrently 

adapt different subcomponents of high–dimensional 

optimization problems. The algorithm is applied on highdimensional 

instances of five widely used test problems with 

very promising results. Comparisons with the standard Particle

Swarm Optimization algorithm are also reported and 

Discussed 

A Population Based Hybrid Meta-heuristic for the 

Uncapacitated Facility Location Problem 

Wayne Pullan 

School of Information and Communication Technology 

Griffith University, Gold Coast, QLD, Australia 

w.pullan@griffith.edu.au 

ABSTRACT 

The uncapacitated facility location problem is one of _nding 

the minimum cost subset of m facilities, where each facility 

has an associated establishment cost, to satisfy the demands 

of n users where the cost of satisfying each user from all possible 

facilities is known. In this paper, PBS, a population 

based metaheuristic for the uncapacitated facility location 

problem is introduced. PBS uses a genetic algorithm based 

meta-heuristic, primarily based on cut and paste crossover 

and directed mutation operators, to generate new starting 

points for a local search. For larger uncapacitated facility 

location instances, PBS is able to e_ectively utilise a number 

of computer processors. It is shown empirically that 

PBS achieves state-of-the-art performance for a wide range 

of uncapacitated facility location benchmark instances. 

Target Tracking and Localization of Binocular Mobile 

Robot using Camshift and SIFT 

Qiu Xuena 

Institute of Automation 

East China University of Science and Technology, 



Lu Qiang 





ABSTRACT 

A real time dynamic target recognition and tracking method is 

presented for mobile robot in this paper. Firstly, the inter-frame 

difference method is applied to detect the moving target. And the 

proposed method computes the color histogram and extracts SIFT 

features in the target region. Then from the following frame, it 

extracts SIFT features, matches with SIFT features extracted from 

target, and calculates the center location of the matched features. 

Finally the Camshift algorithm, starting from the center location, 

is used to track the target. Experiments demonstrate that the 

proposed method can effectively recognize and track the moving 

target, and its performance is better than the classic Camshift 

algorithm. 

Hierarchical Oriented Genetic Algorithms for Coverage





+90 222 2393750 



ABSTRACT 


















A Multi-Objective-Based Non-Stationary UAV Assignment 

Model for Constraints Handling using PSO 

Feng Pan 




+86-10-68948971 


Guanghui Wang 




+86-10-68948971 


Yang Liu 




+86-10-68948971 


ABSTRACT 










model is proposed for the non-stationary problem. The proposed














ABSTRACT 














very promising results. Comparisons with the standard Particle 


discussed. 



Wayne Pullan 




ABSTRACT 
















Robot using Camshift and SIFT

Qiu Xuena 





Lu Qiang 





ABSTRACT 












algorithm. 

Embedded Self-Adaptation to Escape from Local Optima 

Oleg Rokhlenko 

IBM Research Lab., Haifa, Israel 

olegr@il.ibm.com 

Ydo Wexler 

Microsoft Research, Redmond, USA 

ydow@microsoft.com 

ABSTRACT 

Self-adaptation in genetic algorithms has been suggested as 

a strategy to enhance evolutionary algorithms for optimization 

tasks. We consider continuous self-adaptation schemes 

called strategies that are governed by evolutionary rules, and 

suggest to incorporate multiple strategies to improve the 

performance of genetic algorithms. We show that employing 

multiple strategies, and letting evolutionary pressure steer 

adaptation, can overcome the problem of premature convergence. 

To demonstrate the power of our method we apply it 

to optimization problems of uncapacitated facility location. 

The method outperforms both methods with a single strategy 

and previously reported methods on several benchmarks. 

In these runs, algorithms that incorporate multiple strategies 

avoid getting stuck in local optimum, and converge to 

better solutions. 

Bacterial Foraging Optimization Algorithm with Particle 

Swarm Optimization Strategy for Global Numerical 

Optimization 

Hai Shen 

_ 

Key Laboratory of Industrial

Informatics, Shenyang 

Institute of Automation, 

Chinese Academy of 

Sciences, China 

Graduate School of the 



College of Physics Science 

and Technology, Shenyang 

Normal University, China 

shenhai@sia.cn 

Yunlong Zhu 

Key Laboratory of Industrial 





ylzhu@sia.cn 

Xiaoming Zhou 









zhouxiaoming@sia.cn 

Haifeng Guo 






guohf@sia.cn 

Chunguang Chang 






changchunguang@sia.cn 

ABSTRACT 

In 2002, K. M. Passino proposed Bacterial Foraging Optimization 

Algorithm (BFOA) for distributed optimization and control. One of 

the major driving forces of BFOA is the chemotactic movement of 

a virtual bacterium that models a trial solution of the optimization 

problem. However, during the process of chemotaxis, the BFOA 

depends on random search directions which may lead to delay in 

reaching the global solution. Recently, a new algorithm BFOA 

oriented by PSO termed BF-PSO has shown superior in proportional 

integral derivative controller tuning application. In order to 

examine the global search capability of BF-PSO, we evaluate the 

performance of BFOA and BF-PSO on 23 numerical benchmark 

functions. In BF-PSO, the search directions of tumble behavior 

for each bacterium oriented by the individual’s best location and 

the global best location. The experimental results show that BFPSO 

performs much better than BFOA for almost all test functions. 

That’s approved that the BFOA oriented by PSO strategy improve 

its global optimization capability.

The Study of the Knowledge Optimization Tool 

Akira Otsuki 


Keio University Tokyo, Japan, 

cecil@a6.keio.jp 

Kenichi Okada 


Keio University Tokyo, Japan 

okada@ics.keio.ac.jp 

ABSTRACT 

In this study, a tool was constructed that supports the process of 

tying to organize knowledge newly created after tacit knowledge 

has been optimized by applying knowledge management 

strategies, 3C(“Customer”, “Company” and “Competitor” 

Analysis), a marketing mix, and various enumeration methods. 

The tool was verified by quantitative methods, user feedback 

studies, and evaluation through comparison with similar tools. 

Until now, though some theories regarding the organization of 

newly created knowledge have been advocated based on user 

feedback studies, the method of concretely applying such theories 

to real-world business circumstances has not been presented. In 

the current study, the tool was tested through use in an actual 

administrative project and proved to be more effective than an 

already-existing tool used for the organization of newly-created 

knowledge. 

Hierarchical Oriented Genetic Algorithms for Coverage 





+90 222 2393750 



ABSTRACT 


















A Multi-Objective-Based Non-Stationary UAV Assignment 

Model for Constraints Handling using PSO

Feng Pan 




+86-10-68948971 


Guanghui Wang 




+86-10-68948971 


Yang Liu 




+86-10-68948971 


ABSTRACT 










model is proposed for the non-stationary problem. The proposed 














ABSTRACT 














very promising results. Comparisons with the standard Particle 


discussed.



Wayne Pullan 




ABSTRACT 
















Robot using Camshift and SIFT 

Qiu Xuena 





Lu Qiang 





ABSTRACT 












algorithm. 

Embedded Self-Adaptation to Escape from Local Optima 

Oleg Rokhlenko 

IBM Research Lab., Haifa, Israel 

olegr@il.ibm.com 

Ydo Wexler

Microsoft Research, Redmond, USA 

ydow@microsoft.com 

ABSTRACT 

Self-adaptation in genetic algorithms has been suggested as 

a strategy to enhance evolutionary algorithms for optimization 

tasks. We consider continuous self-adaptation schemes 

called strategies that are governed by evolutionary rules, and 

suggest to incorporate multiple strategies to improve the 

performance of genetic algorithms. We show that employing 

multiple strategies, and letting evolutionary pressure steer 

adaptation, can overcome the problem of premature convergence. 

To demonstrate the power of our method we apply it 

to optimization problems of uncapacitated facility location. 

The method outperforms both methods with a single strategy 

and previously reported methods on several benchmarks. 

In these runs, algorithms that incorporate multiple strategies 

avoid getting stuck in local optimum, and converge to 

better solutions. 

Bacterial Foraging Optimization Algorithm with Particle 

Swarm Optimization Strategy for Global Numerical 

Optimization 

Hai Shen 

_ 









College of Physics Science 

and Technology, Shenyang 

Normal University, China 

shenhai@sia.cn 

Yunlong Zhu 






ylzhu@sia.cn 

Xiaoming Zhou 









zhouxiaoming@sia.cn 

Haifeng Guo 


Informatics, Shenyang




guohf@sia.cn 

Chunguang Chang 






changchunguang@sia.cn 

ABSTRACT 

In 2002, K. M. Passino proposed Bacterial Foraging Optimization 

Algorithm (BFOA) for distributed optimization and control. One of 

the major driving forces of BFOA is the chemotactic movement of 

a virtual bacterium that models a trial solution of the optimization 

problem. However, during the process of chemotaxis, the BFOA 

depends on random search directions which may lead to delay in 

reaching the global solution. Recently, a new algorithm BFOA 

oriented by PSO termed BF-PSO has shown superior in proportional 

integral derivative controller tuning application. In order to 

examine the global search capability of BF-PSO, we evaluate the 

performance of BFOA and BF-PSO on 23 numerical benchmark 

functions. In BF-PSO, the search directions of tumble behavior 

for each bacterium oriented by the individual’s best location and 

the global best location. The experimental results show that BFPSO 

performs much better than BFOA for almost all test functions. 

That’s approved that the BFOA oriented by PSO strategy improve 

its global optimization capability. 

Nodes Localization in Sensor Networks based on Vectors 


Wang Yu-feng 


Beihang University 

Beijing 100191, China 

windsand559@163.com 

Wang Yan 




w-yan@buaa.edu.cn 

Mu Chao-yi 

Xi’an Research Institute of 

Applied Optics 


muchmail@163.com 

ABSTRACT 

This paper proposed a design of an integrated algorithm based on 

DV-Hop. A Location Correction Vector (LCV) was constructed 

by the difference between estimated distance and range 

measurement, nodes were clustered when anchors were heads of 

clusters, where object function expressing total distance error was 

constructed in a cluster. Particle Swarm Optimization (PSO) was 

used to solve the minimization problem, then correction steps of 

all member nodes had been done; the value of location correction 

equals the product of LCV and step; then extra location correction 

had been executed by using the relative positions among edge

nodes of neighbor clusters. Simulation results show that the 

localization error of DV-Hop has been reduced by 75% and 

indicate that the present algorithm is also applicable to lowdensity 

networks. 

A Novel Robust Background Modeling Algorithm 

for Complex Natural Scenes 

Wang Zhi-Ling 

Department of Automation 


of China 

Hefei, 230027 

P.R. China 

zlwang3@mail.ustc.edu.cn 

Chen Zong-Hai 

Department of Automation, University 

of Science and Technology of China; 

Hefei, 230027, P.R. China 

National Laboratory of Pattern 

Recognition, Institute of Automation, 

Chinese Academy of Sciences, 

Beijing, 100080, P.R. China 

chenzh@ustc.edu.cn 

Chen Hui-Yong 



of China 

Hefei, 230027 

P.R. China 

jierrychen@ustc.edu 

ABSTRACT 

Background modeling is fundamentally important in the computer 

vision tasks such as image understanding, object tracking and 

video surveillance. It is especially difficult in the complex natural 

scenes, mainly due to two matters: 1) gross errors resulted by 

random outliers that can not be described in a uniform 

distribution; 2) structural confusion cluttered by sample sets’ 

polymorphism, which is originated by multiple structures. For 

dealing with these problems, a novel robust background modeling 

algorithm is presented. The model is established by an improved 

Multi-RANSAC approach for dynamic background pixels and by 

one-tail trimmed sample mean estimator for static pixels. A threecomponent- 

set is derived for the model so that it can be updated 

quickly in a unified framework for both types. It stands right even 

when there are more than 70 percent outliers and is fit for 

complex natural scenes. Quantitative evaluation and comparisons 

with traditional methods show that the proposed method has much 

improved results. 

Dynamic Output Feedback Control of Uncertain Networked 

Control Systems 

Weihua Deng 

Shanghai Key Laboratory of Power Station 

Automation Technology, Shanghai University

dwh197859@126.com 

Minrui Fei 


Automation Technology, Shanghai University 

mrfei@staff.shu.edu.cn 

ABSTRACT 

The paper focuses on the problem of output feedback control 

for uncertain networked control systems(NCSs) that possess 

random time-delay which is described by a Markov process. 

Based on Lyapunov-Razumikhin method a dynamic output 

feedback controller is proposed to stabilize the class of NCSs. 

A su±cient condition for existence of such controller is given 

in terms of bilinear matrix inequalities (BMIs). A modi¯ed 

algorithm is used to solve the BMIs. A numerical example 

illustrates the utility of the proposed approach. 

Evolving Distributed Algorithms with Genetic 

Programming: Election 

Thomas Weise 

Distributed Systems Group 

University of Kassel 

34121 Kassel, Germany 

weise@vs.uni-kassel.de 

Michael Zapf 




zapf@vs.uni-kassel.de 

ABSTRACT 

In this paper, we present a detailed analysis of the application 

of Genetic Programming to the evolution of distributed 

algorithms. This research field has many facets which make 

it especially difficult. These aspects are discussed and countermeasures 

are provided. Six different Genetic Programming 

approaches (SGP, eSGP, LGP, RBGP, eRBGP, and Fraglets) 

are applied to the election problem as case study utilizing 

these countermeasures. The results of the experiments are 

analyzed statistically and discussed thoroughly. 

Why Evolution Is Not a Good Paradigm For Program 

Induction; A Critique of Genetic Programming 

John R. Woodward 

University of Nottingham 

199, Taikang East Road, University Park 

Ningbo, 315100, People’s Republic of China 

John.Woodward @Nottingham.edu.cn 

Ruibin Bai 




Ruibin.Bai @Nottingham.edu.cn 

ABSTRACT 

We revisit the roots of Genetic Programming (i.e. Natural

Evolution), and conclude that the mechanisms of the pro- 

cess of evolution (i.e. selection, inheritance and variation) 

are highly suited to the process; genetic code is an e_ec- 

tive transmitter of information and crossover is an e_ective 

way to search through the viable combinations. Evolution 

is not without its limitations, which are pointed out, and it 

appears to be a highly e_ective problem solver; however we 

over-estimate the problem solving ability of evolution, as it 

is often trying to solve \self-imposed" survival problems. 

We are concerned with the evolution of Turing Equiva- 

lent programs (i.e. those with iteration and memory). Each 

of the mechanisms which make evolution work so well are 

examined from the perspective of program induction. Com- 

puter code is not as robust as genetic code, and therefore 

poorly suited to the process of evolution, resulting in a insur- 

mountable landscape which cannot be navigated e_ectively 

with current syntax based genetic operators. Crossover, has 

problems being adopted in a computational setting, primar- 

ily due to a lack of context of exchanged code. A review of 

the literature reveals that evolved programs contain at most 

two nested loops, indicating that a glass ceiling to what can 

currently be accomplished. 

Topology Optimization of Structures Using Ant Colony 

Optimization 

Chun-Yin Wu 

Department of Mechanical 

Engineering, Tatung University, 

Taipei, Taiwan, R.O.C. 

cywu@ttu.edu.tw 

Ching-Bin Zhang 




g9101010@hotmail.com 

Chi-Jer Wang 




chijerwang@hotmail.com 

Abstract 

A modified ACO algorithm that derives from specific definition 

of pheromone and cooperation mechanism between ants was 

applied for solving topology optimization problem of structure. 

Mesh topology of finite element model for structure was treated 

as possible paths for ant’s movement. A tour on mesh topology 

map for seeking food finished by ant is transformed into a 

structure and the finite element method was applied to analyze the 

structure for calculating pheromone deposited on the path. The 

amount of accumulated pheromone deposited on every element by 

different ants was used to find optimum structural design. From 

the results studied in this paper, the purposed ACO algorithm 

provides as alternate optimization method that has high potential 

in finding the best design for topology optimization of structure 

successfully and efficiently. 

A Global Optimization Based on Physicomimetics

Framework 

Li-Ping Xie 

1 College of Electrical and Information Engineering, 

Lanzhou University of Technology, Lanzhou 730050 

2 Complex System and Computational Intelligence 


Technology, Taiyuan, Shanxi, P.R. China, 030024 

jiangzhou2007@sohu.com 

Jian-Chao Zeng 

Complex System and Computational Intelligence 




ABSTRACT 

Based on physicomimetics framework, this paper presents a 

global optimization algorithm inspired by physics, which is a 

stochastic population-based algorithm. In the approach, each 

physical individual has a position and velocity which move 

through the feasible region of global optimization problem under 

the influence of gravity. The virtual mass of each individual 

corresponds to a user-defined function of the value of an objective 

function to be optimized. An attraction-repulsion rule is 

constructed among individuals and utilized to move individuals 

towards the optimality. Experimental simulations show that the 

algorithm is effective. 

Nodes Localization in Sensor Networks based on Vectors 


Wang Yu-feng 




windsand559@163.com 

Wang Yan 




w-yan@buaa.edu.cn 

Mu Chao-yi 

Xi’an Research Institute of 

Applied Optics 


muchmail@163.com 

ABSTRACT 

This paper proposed a design of an integrated algorithm based on 

DV-Hop. A Location Correction Vector (LCV) was constructed 

by the difference between estimated distance and range 

measurement, nodes were clustered when anchors were heads of 

clusters, where object function expressing total distance error was 

constructed in a cluster. Particle Swarm Optimization (PSO) was 

used to solve the minimization problem, then correction steps of 

all member nodes had been done; the value of location correction 

equals the product of LCV and step; then extra location correction 

had been executed by using the relative positions among edge 

nodes of neighbor clusters. Simulation results show that the 

localization error of DV-Hop has been reduced by 75% and 

indicate that the present algorithm is also applicable to lowdensity 

networks.

A Novel Robust Background Modeling Algorithm 

for Complex Natural Scenes 

Wang Zhi-Ling 



of China 

Hefei, 230027 

P.R. China 

zlwang3@mail.ustc.edu.cn 

Chen Zong-Hai 

Department of Automation, University 

of Science and Technology of China; 

Hefei, 230027, P.R. China 

National Laboratory of Pattern 

Recognition, Institute of Automation, 

Chinese Academy of Sciences, 

Beijing, 100080, P.R. China 

chenzh@ustc.edu.cn 

Chen Hui-Yong 



of China 

Hefei, 230027 

P.R. China 

jierrychen@ustc.edu 

ABSTRACT 

Background modeling is fundamentally important in the computer 

vision tasks such as image understanding, object tracking and 

video surveillance. It is especially difficult in the complex natural 

scenes, mainly due to two matters: 1) gross errors resulted by 

random outliers that can not be described in a uniform 

distribution; 2) structural confusion cluttered by sample sets’ 

polymorphism, which is originated by multiple structures. For 

dealing with these problems, a novel robust background modeling 

algorithm is presented. The model is established by an improved 

Multi-RANSAC approach for dynamic background pixels and by 

one-tail trimmed sample mean estimator for static pixels. A threecomponent- 

set is derived for the model so that it can be updated 

quickly in a unified framework for both types. It stands right even 

when there are more than 70 percent outliers and is fit for 

complex natural scenes. Quantitative evaluation and comparisons 

with traditional methods show that the proposed method has much 

improved results. 

Dynamic Output Feedback Control of Uncertain Networked 

Control Systems 

Weihua Deng 



dwh197859@126.com 

Minrui Fei 

Shanghai Key Laboratory of Power Station


mrfei@staff.shu.edu.cn 

ABSTRACT 

The paper focuses on the problem of output feedback control 

for uncertain networked control systems(NCSs) that possess 

random time-delay which is described by a Markov process. 

Based on Lyapunov-Razumikhin method a dynamic output 

feedback controller is proposed to stabilize the class of NCSs. 

A su±cient condition for existence of such controller is given 

in terms of bilinear matrix inequalities (BMIs). A modi¯ed 

algorithm is used to solve the BMIs. A numerical example 

illustrates the utility of the proposed approach. 

Evolving Distributed Algorithms with Genetic 

Programming: Election 

Thomas Weise 




weise@vs.uni-kassel.de 

Michael Zapf 




zapf@vs.uni-kassel.de 

ABSTRACT 

In this paper, we present a detailed analysis of the application 

of Genetic Programming to the evolution of distributed 

algorithms. This research field has many facets which make 

it especially difficult. These aspects are discussed and countermeasures 

are provided. Six different Genetic Programming 

approaches (SGP, eSGP, LGP, RBGP, eRBGP, and Fraglets) 

are applied to the election problem as case study utilizing 

these countermeasures. The results of the experiments are 

analyzed statistically and discussed thoroughly. 

Canonical Representation Genetic Programming 






Ruibin Bai 





ABSTRACT 

Search spaces sampled by the process of Genetic Program- 

ming often consist of programs which can represent a func- 

tion in many di_erent ways. Thus, when the space is exam- 

ined it is highly likely that di_erent programs may be tested 

which represent the same function, which is an undesirable 

waste of resources. It is argued that, if a search space can 

be constructed where only unique representations of a func- 

tion are permitted, then this will be more successful than

employing multiple representations. When the search space 

consists of canonical representations it is called a canoni- 

cal search space, and when Genetic Programming is applied 

to this search space, it is called Canonical Representation 

Genetic Programming. 

The challenge lies in constructing these search spaces. 

With some function sets this is a trivial task, and with some 

function sets this is impossible to achieve. With other func- 

tion sets it is not clear how the goal can be achieved. In 

this paper, we speci_cally examine the search space de_ned 

by the function set f+; ; _; =g and the terminal set fx; 1g. 

Drawing inspiration from the fundamental theorem of arith- 

metic, and results regarding the fundamental theorem of al- 

gebra, we construct a representation where each function 

that can be constructed with this primitive set has a unique 

representation. 

Why Evolution Is Not a Good Paradigm For Program 

Induction; A Critique of Genetic Programming 






Ruibin Bai 





ABSTRACT 

We revisit the roots of Genetic Programming (i.e. Natural 

Evolution), and conclude that the mechanisms of the pro- 

cess of evolution (i.e. selection, inheritance and variation) 

are highly suited to the process; genetic code is an e_ec- 

tive transmitter of information and crossover is an e_ective 

way to search through the viable combinations. Evolution 

is not without its limitations, which are pointed out, and it 

appears to be a highly e_ective problem solver; however we 

over-estimate the problem solving ability of evolution, as it 

is often trying to solve \self-imposed" survival problems. 

We are concerned with the evolution of Turing Equiva- 

lent programs (i.e. those with iteration and memory). Each 

of the mechanisms which make evolution work so well are 

examined from the perspective of program induction. Com- 

puter code is not as robust as genetic code, and therefore 

poorly suited to the process of evolution, resulting in a insur- 

mountable landscape which cannot be navigated e_ectively 

with current syntax based genetic operators. Crossover, has 

problems being adopted in a computational setting, primar- 

ily due to a lack of context of exchanged code. A review of 

the literature reveals that evolved programs contain at most 

two nested loops, indicating that a glass ceiling to what can 

currently be accomplished. 

Topology Optimization of Structures Using Ant Colony 

Optimization 

Chun-Yin Wu 


Engineering, Tatung University,


cywu@ttu.edu.tw 

Ching-Bin Zhang 




g9101010@hotmail.com 

Chi-Jer Wang 




chijerwang@hotmail.com 

Abstract 

A modified ACO algorithm that derives from specific definition 

of pheromone and cooperation mechanism between ants was 

applied for solving topology optimization problem of structure. 

Mesh topology of finite element model for structure was treated 

as possible paths for ant’s movement. A tour on mesh topology 

map for seeking food finished by ant is transformed into a 

structure and the finite element method was applied to analyze the 

structure for calculating pheromone deposited on the path. The 

amount of accumulated pheromone deposited on every element by 

different ants was used to find optimum structural design. From 

the results studied in this paper, the purposed ACO algorithm 

provides as alternate optimization method that has high potential 

in finding the best design for topology optimization of structure 

successfully and efficiently 

A Global Optimization Based on Physicomimetics 

Framework 

Li-Ping Xie 

1 College of Electrical and Information Engineering, 

Lanzhou University of Technology, Lanzhou 730050 

2 Complex System and Computational Intelligence 



jiangzhou2007@sohu.com 

Jian-Chao Zeng 

Complex System and Computational Intelligence 




ABSTRACT 

Based on physicomimetics framework, this paper presents a 

global optimization algorithm inspired by physics, which is a 

stochastic population-based algorithm. In the approach, each 

physical individual has a position and velocity which move 

through the feasible region of global optimization problem under 

the influence of gravity. The virtual mass of each individual 

corresponds to a user-defined function of the value of an objective 

function to be optimized. An attraction-repulsion rule is 

constructed among individuals and utilized to move individuals 

towards the optimality. Experimental simulations show that the 

algorithm is effective. 

The Stability Study of Biped Robot Based

on GA and Neural Network 

Lun Xie 



Beijing, Beijing 100083, P.R. China 

13681560734 

ygao@tsinghua.edu.cn 

Zhiliang Wang 




13910727340 

wzl@ies.ustb.edu.cn 

Kun Wu 




15010272215 

wukunshe@163.com 

ABSTRACT 

In recent years, the Biped Robot is more and more selfdetermining 

and time-sensitive, so the stability has become a very 

important question. But the traditional control methods can not 

meet it. To solve this question, Artificial Neural Network (ANN) 

has been brought up. Instead of most traditional control methods, 

Artificial Neural Network is applied widely to control the Biped 

Robot to walk accurately and stably. In this work, we design a 

control system of the Biped Robot with GA-ANN (Artificial 

Neural Network based on Genetic Algorithm). The GA-ANN 

control system adjusts the weights by the robot’s Zero Moment 

Point (ZMP), tracks the robot’s nonlinear kinetic system and 

keeps the robot step stably. Experiments show the stability 

improvement of robot using proposed algorithm. 

Problem Difficulty Analysis for Particle Swarm 

Optimization: Deception and Modality 

Bin Xin 

Laboratory of Complex System 

Intelligent Control and Decision 

China Ministry of Education 



Beijing, 100081, China 

86-10-68912463 

brucebin@bit.edu.cn 

Jie Chen 

Laboratory of Complex System 






86-10-68913748 

chenjie@bit.edu.cn 

Feng Pan 

Laboratory of Complex System






86-10-68912463 


ABSTRACT 

This paper studies the problem difficulty for a popular 

optimization method - particle swarm optimization (PSO), 

particularly for the PSO variant PSO-cf (PSO with constriction 

factor), and analyzes its predictive measures. Some previous 

measures and related issues about other optimizers, mainly 

including deception and modality, are checked for PSO. It is 

observed that deception is mainly the combination of three factors 

– the measure ratios of attraction basins, the relative distance of 

attractors and the relative difference of attractors’ altitudes. 

Multimodality and multi-funnel are proved not to be the essential 

factors contributing to the problem difficulty for PSO. The 

counterexamples and comparative experiments in this paper can 

be taken as a reference for further researches on novel 

comprehensive predictive measures of problem difficulty for PSO. 

On Average Time Complexity of Evolutionary Negative 

Selection Algorithms for Anomaly Detection 

Baoliang Xu1,2, Wenjian Luo1,2, Xingxin Pei1,2, Min Zhang1,2, Xufa Wang1,2 

1Nature Inspired Computation and Applications Laboratory, 

Department of Computer Science and Technology, 

University of Science and Technology of China, Hefei, 230027, Anhui, China 

2Anhui Key Laboratory of Software in Computing and Communication, 

University of Science and Technology of China, Hefei 230027, China 

xubaol@mail.ustc.edu.cn, wjluo@ustc.edu.cn, michaelp@mail.ustc.edu.cn, 

zhangmin@mail.ustc.edu.cn, xfwang@ustc.edu.cn 

ABSTRACT 

Evolutionary Negative Selection Algorithms have been proposed 

and used in artificial immune system community for years. However, 

there are no theoretical analyses about the average time 

complexity of such algorithms. In this paper, the average time 

complexity of Evolutionary Negative Selection Algorithms for 

anomaly detection is studied, and the results demonstrate that its 

average time complexity depends on the self set very much. Some 

simulation experiments are done, and it is demonstrated that the 

theoretical results approximately agree with the experimental 

results. The work in this paper not only gives the average time 

complexity of Evolutionary Negative Selection Algorithms for the 

first time, but also would be helpful to understand why different 

immune responses (i.e. primary/cross-reactive/secondary immune 

response) in biological immune system have different efficiencies. 

Adaptive Immune Genetic Algorithm for Logic Circuit 

Design 

Hai-Qin Xu 




Shanghai 201620, P. R. China 

xuhaiqin@dhu.edu.cn

Yong-Sheng Ding* 




Engineering Research Center of 

Digitized Textile & Fashion 

Technology, Ministry of Education 


* ysding@dhu.edu.cn 

Zhi-Hua Hu 





zhihuah0115@yahoo.com.cn 

ABSTRACT 

Evolutionary design of circuits (EDC), an important branch of 

evolvable hardware which emphasizes circuit design, is a 

promising way to realize automated design of electronic circuits. 

In order to improve the evolutionary design of logic circuits in a 

more efficient, scalable and capable way, an Adaptive Immune 

Genetic Algorithm (AIGA) was designed. The AIGA draws into 

the mechanisms in biological immune systems such as clonal 

selection, hypermutation, and immune memory. Besides, the 

AIGA features an adaptation strategy that enables crossover 

probability and mutation probability to vary with genetic-search 

process. Our results are compared with those produced by the 

Multi-objective Evolutionary Algorithm (MOEA) and the Simple 

Immune Algorithm (SIA). The simulation results show that AIGA 

overcomes the disadvantages of premature convergence, and 

improves the global searching efficiency and capability. 

Energy-saving Control of Greenhouse Climate Based on 

MOCC Strategy ¤ 

Lihong Xu 

y 

Member,ACM 





xulhk@163.com 

Haigen Hu 

z 





hnhhg@163.com 

Bingkun Zhu 





ABSTRACT 

The adjustment of greenhouse environment has heavy in- 

°uence on the plants growth, production yield, quality and 

energy consumption. Moreover, classical methods used for

solving greenhouse environment multi-objective control prob- 

lems may be more reasonable by adopting "region" control 

objectives instead of "point" control objectives. In this pa- 

per, we propose a novel energy-saving control algorithm, and 

employ Multi-Objective Compatible Control(MOCC) strat- 

egy and an extant greenhouse model to optimize the con- 

trol parameters of greenhouse environment for short time- 

scale prediction(15 minutes). A series of optimization exper- 

iments using Multi-Objective Evolutionary Algorithms(MO- 

EAs) are presented to minimize energy consumption under 

certain compatible control "region" conditions. The results 

are encouraging, and show that the proposed method may 

be valuable and helpful to formulate environmental control 

strategies, to pursue less energy cost, and to gradually re- 

alize the ultimate objectives of environmental optimal con- 

trol. 

An Improved MOCC with Feedback Control Structure Based 

on Preference 

Lihong Xu 

Member, ACM 



Tongji University, Shanghai,China, 200092 

xulhk@163.comBingkun Zhu 



Tongji University, Shanghai, China, 200092 

zhu1981_2001@yahoo.com.cn 

Erik D.Goodman 

Departement of Electrical and Computer Engineering 

Michigan State University East Lansing, MI, USA, 48824 

goodman@egr.msu.edu

ABSTRACT 

The optimal solution of multi-objective control problem (MOCP) isn’t unique, so it is hard for traditional method to obtain these optimal solutions in 

one simulation process. Based on this background, Multi-Objective Compatible Control (MOCC) algorithm was presented by Lihong Xu in [2]. 

MOCC is a compromise method, which hunts for suboptimal and feasible region as the control aim rather than precise optimal point. The controller 

of MOCC is optimized by GA in its interval, namely its controller lacks concrete controller structure. Due to the controller without concrete structure, 

the system model must be accurate and without input disturbance; however, it is impractical in practice. Besides, the control problem is different from 

the optimization. Different user has different preference and users’ preference information plays a key role in control performance. In this paper, the 

feedback control law uLand users’ preference information are incorporated into MOCC algorithm. An improved MOCC (IMOCC) algorithm is 

presented. The simulation result demonstrates its superiority and advantage over the MOCC algorithm. 

Association Based Immune Network for Multimodal 

Function Optimization 

Qingzheng Xu1,2 

xuqingzheng@hotmail.com 

Jing Si1 

sijing606084@163.com 

Lei Wang1 

leiwang@xaut.edu.cn 

1School of Computer Science and Engineering 

Xi’an University of Technology 


2Xi’an Communication Institute 


ABSTRACT 

For the problem of serious resources waste, indeterminate 

direction of local search and degeneration in the original optaiNet, 

a novel association based immune network is proposed for 

multimodal function optimization. The hexabasic model mimics 

natural phenomenon in immune system such as clonal selection, 

affinity maturation, immune network, immune memory and 

immune association. The antibody population scale is semi-fixed 

reducing the time and space required to execute it. The 

information of the antibody population and the memory cells 

population is effective utilized to point out the direction of local 

search, to regulate the ratio between local search and global 

search, and to enhance the affinity of new antibodies. The elitist 

selection mechanism is adopted to ensure the convergence and 

stability of our algorithm respectively. The experiments on 10 

benchmark functions show that when compared with opt-aiNet 

method, the new algorithm is capable of improving the search 

performance significantly in global convergence, convergence 

speed, computational cost, search ability, solution quality and 

algorithm stability. 

A Genetic Algorithm-based Feature Selection Method 

for Human Identification based on Ground Reaction Force 

Su Xu 

1. The Key laboratory of Biomimetic 

Sensing and Advanced Robot 

Technology, Institute of 

Intelligence Machines, Chinese 

Academy of Science, Hefei, Anhui, 

230031 

2. Dept. of Automation 

University of Science and 

Technology of China, Hefei,

Anhui, 230027 

+86-0551-3620494 

ashesxu@mail.ustc.edu.cn 

Xu Zhou 

The Key laboratory of Biomimetic 


Technology, 

Institute of Intelligence Machines, 

Chinese Academy of Science, 

Hefei, Anhui, 230031 

xzhou@iim.ac.cn 

Yi-ning Sun 

The Key laboratory of Biomimetic 


Technology, 

Institute of Intelligence Machines, 

Chinese Academy of Science, 

Hefei, Anhui, 230031 

ynsun@iim.ac.cn 

ABSTRACT 

Biometrics-based identification is a promising technology. 

Ground reaction force (GRF), with its characteristics of 

non-invasion, easily measurement and low environment-affection, 

shows a potential in this field. Feature selection is an important 

step in biometrics-based identification. In this paper, a genetic 

algorithm-based feature selection method was discussed. The 

proposed algorithm has the advantage of finding small subsets of 

features that perform well in identification. Two contrast 

experiments were conducted to show the effectiveness of the 

algorithm, which shows that with GA, higher identification 

accuracy and smaller feature size were reached.、 

A Hybrid Particle Swarm Optimization Approach with Prior 

Crossover Differential Evolution 

Wei Xu 




86-21-64252576 

xuweiecust@tom.com 

Xingsheng Gu* 




86-21-64252576 


ABSTRACT 

Particle swarm optimization (PSO) is population-based heuristic 

searching algorithm. PSO has excellent ability of global 

optimization. However, there are some shortcomings of 

prematurity, low convergence accuracy and speed, similarly to 

other evolutionary algorithms (EA). To improve its performance, 

a hybrid particle swarm optimization is proposed in the paper. 

Firstly, the average position and velocity of particles are 

incorporated into basic PSO for concerning with the effect of the 

evolution of the whole swarm. Then a differential evolution (DE) 

computation, which introduces an extra population for prior

crossover, is hybridized with the improved PSO to form a novel 

optimization algorithm, PSOPDE. The role of prior crossover is to 

appropriately diversify the population and increase the probability 

of reaching better solutions. DE component takes into account the 

stochastic differential variation, and enhances the exploitation in 

the neighborhoods of current solutions. PSOPDE is implemented 

on five typical benchmark functions, and compared with six other 

algorithms. The results indicate that PSOPDE behaves better, and 

greatly improve the searching efficiency and quality. 

Parameter Estimation for Asymptotic Regression Model by 

Particle Swarm Optimization 

Xing Xu 

State Key Lab. of Software 

Engineering, Wuhan 

University 

Wuhan, China 

whuxx84@yahoo.com.cn 

Yuanxiang Li 



University 

Wuhan, China 

yxli@whu.edu.cn 

Yu Wu 



University 

Wuhan, China 

wy08_whu@yahoo.com.cn 

Xin Du 



University 

Wuhan, China 

xindu79@126.com 

ABSTRACT 

Asymptotic regression model (ARM) has been widely used 

in the field of agriculture, biology and engineering, especially 

in agriculture. Parameter estimation for ARM is a significant, 

challenging and difficult issue. The modern heuristic 

algorithm has been proved to be a highly effective and successful 

technique in parameter estimation of nonlinear models. 

As a novel evolutionary computation paradigm based 

on social behavior of bird flocking or fish schooling, particle 

swarm optimization (PSO) has shown outstanding performance 

in many real-world applications, for it is conceptually 

simple and practically easy to be implemented. In 

the present work, parameters of ARM are estimated on the 

basis of PSO for the first time. Firstly, PSO is compared 

with evolutionary algorithm (EA) on seven groups of actual 

data; PSO, while using less number of function evaluations, 

can find a parameter set as well as EA. Secondly, 

we estimate one-dimensional, two-dimensional and threedimensional 

parameter by fixing two, one and zero of all 

parameters of ARM, respectively. Finally, how sampling 

range and data with Gaussian noise influence on the performance 

of PSO is considered. Experimental results show that 

PSO is a stable, reliable and effective method in parameter

estimation for ARM and it’s robust to noise. 

Enhancing Automated Red Teaming with Evolvable 

Simulation 

YongLiang Xu 


Engineering, Nanyang 

Technological University 

Nanyang Avenue 


y050063@ntu.edu.sg 

Malcolm Yoke Hean Low 


Engineering, Nanyang 

Technological University 

Nanyang Avenue 


yhlow@ntu.edu.sg 

Chwee Seng Choo 


20 Science Park Drive 


cchweese@dso.org.sg 

ABSTRACT 

Automated Red Teaming (ART), an automated process for 

Manual Red Teaming, is a technique frequently utilised by 

the Military Operational Analysis (OA) community to uncover 

vulnerabilities in operational tactics. Currently, individual 

ART studies are limited to the parameter tuning of 

a simulation model with a fixed structure. The effects in 

the evolutions of structural features of a simulation model 

have not been investigated in any of the studies. This paper 

investigates the benefits of Evolvable Simulation, which involves 

evolution of the structure of a simulation model. The 

case study used for this purpose is a maritime based scenario 

which involves the defense of an anchorage. Simulation 

results obtained through Evolvable Simulation revealed 

that the quality of the solutions found given an appropriate 

amount of evaluations will improve when the simulation 

model is evolved. Additionally, experimental results 

also showed that it is likely to have negligible improvement 

in solutions for models with smaller search space when the 

amount of evaluations is more than required. The insights 

obtained in this work shows that evolvable simulation is an 

effective methodology which allows decision makers to enhance 

their understanding on military operational tactics. 

Research on Job Shop Scheduling under Uncertainty 

Xu Zhenhao 

East China Univ. of Sci. & Tech. 130 Meilong Road Xuhui District, Shanghai, China 86-21-64252576 

xuzhenhao@ecust.edu.cn Gu Xingsheng 

East China Univ. of Sci. & Tech. 130 Meilong Road Xuhui District, Shanghai, China 86-21-64253463


Gu Jinwei 

East China Univ. of Sci. & Tech. 130 Meilong Road Xuhui District, Shanghai, China 86-21-64252576 


Jiao Bin 

Shanghai Dianji Univ. 690 Jiangchuan Road Minhang District, Shanghai, China 86-21-54758615 


ABSTRACT 

In many real world applications, the processing time of products in Job Shop scheduling problems is not a fixed value, and may vary dynamically 

with the situation. In this study, the scheduling mathematical model of Job Shop problems with uncertain processing time has been established based 

on fuzzy programming theory. The uncertain processing time can be described by the triangular fuzzy numbers, and the Maximum Membership 

Functions of Mean Value method is applied to convert the fuzzy scheduling model to the general optimization model. Furthermore, a fuzzy immune 

scheduling algorithm combined with the feature of the Immune Algorithm is proposed, which can prevents the possibility of stagnation in the 

iteration process and achieves fast convergence for global optimization. The effectiveness and efficiency of the fuzzy scheduling model and the 

proposed algorithm are demonstrated by simulation results. 

PSO Algorithm for a Scheduling Parallel Unit Batch 

Process with Batching 

Ping Yan 

The Key Laboratory of Integrated Automation of 

Process Industry, 


Shenyang 110004, PR China 

+86-13609877928 

0316yanping@126.com 

Lixin Tang 

Liaoning Key Laboratory of Manufacturing System 

and Logistics, 

The Logistics Institute, Northeastern University 

Shenyang 110004, PR China 

+86-24-83680169 

lixintang@mail.neu.edu.cn 

ABSTRACT 

In this paper, a parallel unit batch process scheduling problem 

(PBPSP) integrating batching decision is investigated. The batch 

scheduling problem is to convert the demands for products into 

sets of batches and schedule these batches on the units such that 

makespan is minimized. We propose a Particle Swarm 

Optimization (PSO) algorithm to solve this problem where a 

novel particle solution representation is designed for representing 

a batching scheme for PBPSP and a scale-based repair procedure 

is introduced to make particles feasible. In addition, the proposed 

PSO is combined with a relatively current evolutionary algorithm 

known as Differential Evolution (DE) for enhance the 

performance of PSO. A mixed integer linear programming (MILP) 

formulation is also given and used to calculate a lower bound for 

comparison with the PSO solutions. Computational results 

indicated the validity and effectiveness of the proposed PSO. 

Design and Analysis of Switching Full-order Current 

Observer and Separation Principle for T-S Fuzzy System

ShiYu Yan 

∗ 

State Key Laboratory of Intelligent Technology 

and Systems 


Technology, Tsinghua University 

Beijing, 100084, People’s Republic of China 

ourrockrain@163.com 

ZengQi Sun 

State Key Laboratory of Intelligent Technology 

and Systems 


Technology, Tsinghua University 


National Laboratory of Space Intelligent Control 


szq-dcs@tsinghua.edu.cn 

ABSTRACT 

As the important issues in fuzzy control system, some studies 

on fuzzy observer and separation principle for total fuzzy 

system have been done up to now. However, these existing 

results are far from enough. In order to supplement such 

theoretical study, this paper gives the design and analysis of 

switching fuzzy full-order current observer and proves that 

corresponding separation principle does hold. At last, a numerical 

simulation and comparison with smooth fuzzy fullorder 

prediction observer is given to assess switching fuzzy 

full-order current observer and the truth of the separation 

principle. 

A Real-time Schedule Method for Aircraft Landing 

Scheduling Problem Based on Cellular Automaton 

Shenpeng Yu1, Xianbin Cao1, Maobin Hu2, Wenbo Du1, Jun Zhang3 

1 Department of Computer Science and Technology, University of Science and Technology of China, Hefei, 230026, 

P.R.China, Anhui Province Key Laboratory of Software in Computing and Communication, Hefei, 230026, P.R.China 

86-551-3601545 

ysp@mail.ustc.edu.cn, xbcao@ustc.edu.cn, wenbodu@mail.ustc.edu.cn 

2 School of Engineering Science, University of Science and Technology of China Hefei 230026 P.R.China 

86-551-3600127 

humaobin@ustc.ed.cn 

3 School of Electronic and Information Engineering, Beihang University, Beijing, 100083, P.R.China 

bauuzhangjun@vip.sina.com 

ABSTRACT 

The Aircraft Landing Scheduling (ALS) problem is a typical hard 

multi-constraint optimization problem. In real applications, it is 

not most important to find the best solution but to provide a 

feasible landing schedule in an acceptable time. We propose a 

novel approach which can effectively solve the ALS while 

satisfying the real-time need. It consists of two steps: (i) Use CA 

to simulate the landing process in the terminal airspace and to find 

a considerably good landing sequence; (ii) a simple Genetic 

Algorithm associated with a Relaxation Operator is used to obtain 

a better result based on the CA result. Experiments have shown 

that our method is much faster and suitable for real-time ALS 

problem compared with traditional optimization methods. For all 

the 13 data sets, the proposed approach can find satisfactory 

solutions in less than 2 seconds. 

A Parallel Evolutionary Algorithm for Optimal Pulse-Width

Modulation Technique in Power Systems 

RongXiang Yuan 

School of Electrical Engineering 

Wuhan University 


rxyuan@whu.edu.cn 

Xiufen Zou* 

School of Mathematics & Statistics 



xfzou@whu.edu.cn 

Chunlin Xu 

School of Mathematics & Statistics 



xclxxl414@163.com 

ABSTRACT 

Pulse-Width Modulation (PWM) based on the elimination of loworder 

harmonics needs to deal with a class of systems of nonlinear 

equations whose right-hand terms are changed with time, moreover, 

these systems of nonlinear equations often have multiple solutions 

which are difficult to handle with conventional techniques. In this 

paper, an effective asynchronous parallel evolutionary algorithm is 

proposed to solve these systems of nonlinear equations so that we 

can obtain the switching angles for eliminating the low-order 

harmonics, accordingly, pulse patterns are optimized. In the paper, 

we give the detailed description of parallel algorithm, and the 

numerical results show that we can obtain the switching angles for a 

set of amplitudes and phase angles of the low-order harmonics. 

Mathematical Model and Hybrid Particle Swarm 

Optimization for Flexible Job-Shop Scheduling Problem 

Zeng Ling-li, Zou Feng-xing, Xu Xiao-hong 

College of Mechatronics and Automation, National University of Defense Technology 

410073 Changsha, P.R.China 

86 731 4573370 

zll840915@yahoo.com.cn 

ABSTRACT 

In this paper, A hybrid integer programming model is proposed for 

flexible job-shop scheduling problem(FJSP). Using crossover 

operator and mutation operator, the hybrid particle swarm 

optimization(HPSO) algorithm with simple particle swarm 

optimization(SPSO) algorithm and genetic algorithm(GA) is 

employed to solve this problem. Compared with SPSO algorithm, 

HPSO algorithm has a potential to reach a better optimum. The 

results of simulation indicate that, HPSO algorithm out performs 

SPSO algorithm on searching speed for global optimum and 

avoiding prematurity. 

A Novel Sexual Adaptive Genetic Algorithm Based on 

Two-step Evolutionary Scenario of Baldwin Effect and 

Analysis of Global Convergence 

Mingming Zhang, Shuguang Zhao, Xu Wang 

College of Information Science and Technology 

Donghua University


mmzhang@mail.dhu.edu.cn 

ABSTRACT 

This work presents a novel sexual adaptive genetic algorithm 

(NSAGA) based on two-step evolutionary scenario of Baldwin 

effect to overcome the shortcomings of traditional genetic 

algorithms, such as premature convergence, stochastic roaming, 

and poor capabilities in local exploring. NSAGA simulates sexual 

reproduction in nature and utilizes an effective gender 

determination method to divide the evolutionary population into 

two different gender subgroups. Based on the competition, 

cooperation, and innate differences between two gender 

subgroups, NSAGA adaptively adjusts the sexual genetic 

operators. To guide the individuals’ evolution, NSAGA adopts a 

two-step evolutionary scenario: NSAGA guides individuals in 

niche to forward or reverse evolutionary learning inspired by the 

acquired reinforcement learning theory based on Baldwin effect, 

and enables the transmission of fitness information between 

parents and offspring to supervise the offspring’s evolution. Then, 

the global convergence analysis of NSAGA is presented in detail. 

It is theoretically proved that NSAGA can converge to the global 

optimum and the epsilon-optimal solution with probability one. 

Moreover, numerical simulations are conducted for a set of 

benchmark test functions, and the performance of NSAGA is 

compared with that of some evolutionary algorithms published 

recently. Experiments results show that the proposed algorithm is 

effective and advantageous. 

An Immune Evolutionary Algorithm Based Pose 

Estimation Method for Parallel Manipulator 

Shu-Ping Zhang 




Shanghai ,China 

201620 

spzhang@mail.dhu.edu.cn 

Yong-Sheng Ding 


and Technology; Engineering 

Research Center of Digitized Textile 

& Fashion Technology, 

Ministry of Education 



201620 


Kuang-Rong Hao 





201620 

krhao@dhu.edu.cn 

ABSTRACT 

Based on immune systems, a new immune evolutionary algorithm 

(IEA) is presented to develop a pose estimation method for a 

parallel manipulator in the paper. Four vertices of a parallelogram 

device on a parallel manipulator’s end-effector are used as the

object model. And the problem of pose identification is 

transformed to obtain the optimal depth estimations of the object 

model. In IEA, depth estimations of the object model are taken as 

an antigen. Then the optimal solutions are searched by clone 

selection and variation operator. In theory, this method enriches 

the pose estimation methods from four points correspondences. In 

addition, it provides guidance for practical applications of a 

parallel manipulator. Experiments results demonstrate that our 

algorithm works speedily and robustly. 

An Immune Co-Evolutionary Algorithm Based Approach 

for Optimization Control of Gas Turbine 

Xiang-feng Zhang 

College of Electric 

Shanghai Dianji University 


86-21-64300980-3071 

zxfly537@163.com 

Jun Liu 

College of Electric 

Shanghai Dianji University 


86-21-64300980-3159 

liujun@sdju.edu.cn 

Yong-sheng Ding 

Information Sciences and 

Technology, Donghua University 


86-21-67792329 


ABSTRACT 

Gas turbine is a complex non-linearity system and operates in 

variable conditions. Traditional control methods are usually 

adopted in the control loop of gas turbine. The methods may cause 

control error with the theoretically correct value. In this paper, an 

immune co-evolutionary algorithm (ICEA) is proposed inspired by 

immune mechanisms and co-evolutionary computation. And the 

control of gas turbine is optimized with the ICEA. The procedures 

of the ICEA mainly include clonal selection and proliferation, 

fitness evaluation, hyper-mutation, co-evolution and antibody 

population update. The fitness function is defined referencing to 

the control model of gas turbine considering some constraints, such 

as the compressor surge edge constraints and the highest initial gas 

temperature. Two cases are simulated using the ICEA when the 

system is accelerated to the partial load and the maximum load, 

respectively. The simulations show that the ICEA can optimize the 

quantity of oil to make the gas turbine system reach the terminal 

status within the shortest time. And the consumed time for the 

latter is longer than that for the former. The results demonstrate 

that the ICEA has good feasibility and practicability for the 

optimization control of gas turbine. 

A Hybrid Optimization Algorithm for the Job-shop Scheduling 

Problem

Qiang Zhou 

Department of Computer Science and Technology 

Chuzhou University, Chuzhou, China, 239012 

Tel: +86-0550-3047526 

aq_ay@126.com 

Xunxue Cui 

New Star Research Institute of Applied Technology 

Hefei, China, 230031 

Tel: +86-0551-5769700 

xxcui@tsinghua.org.cn 

Zhengshan Wang 



Tel: +86-0550-3510481 

zswang@chzu.edu.cn 

Bin Yang 



Tel: +86-0550-3510481 

ybcub@126.com 

ABSTRACT 

The job-shop scheduling problem is a NP-hard combinational optimization and one of the best-known machine scheduling problems. Genetic 

algorithm is an effective search algorithm to solve this problem; however the quality of the best solution obtained by the algorithm has to improve 

due to its limitation. The paper proposes a novel hybrid optimization algorithm for the job-shop scheduling problem, which applies chaos theory on 

the basis of combining genetic programming and genetic algorithm. It improves the quality of the initial population by using chaos optimization 

method; it maintains the population diversity by chaotic disturbance and anti-equilibration in crossover of genetic programming. Three traversals are 

adopted to reduce the chance of reaching local optimal solution. Moreover, a scheme of changing weight is proposed during the process of evolution 

to increase the global exploration capability. The experimental results show that the effectiveness and good quality of the hybrid algorithm is obvious 

from some benchmarks. 

A Study of Parallel Evolution Strategy – Pattern Search on 

a GPU Computing Platform 

Weihang Zhu 

Department of Industrial Engineering 

Lamar University 

P.O.Box 10032 

Beaumont, Texas 77710, USA 

1-409-880-8876 

Weihang.Zhu@lamar.edu 

ABSTRACT 

This paper presents a massively parallel Evolution Strategy – 

Pattern Search Optimization (ES-PS) algorithm with graphics 

hardware acceleration on bound constrained nonlinear continuous 

optimization functions. The algorithm is specifically designed for 

a graphic processing unit (GPU) hardware platform featuring 

‘Single Instruction – Multiple Thread’ (SIMT). GPU computing 

is an emerging desktop parallel computing platform. The hybrid 

ES-PS optimization method is implemented in the GPU 

environment and compared to a similar implementation on CPU 

hardware. Computational results indicate that GPU-accelerated 

SIMT-ES-PS method is orders of magnitude faster than the

corresponding CPU implementation. The main contribution of 

this paper is the parallelization analysis and performance analysis 

of the hybrid ES-PS with GPU acceleration. 

A Proposed Modularized DNA Computer, 

Based on Biochips 

Ying Zhu1 


2999 Renmin R. (N.), 


Ying.Zhu@csiro.au 

Yongsheng Ding 


2999 Renmin R. (N.), 



Wanggen Li 


2999 Renmin R. (N.), 


wgli@mail.dhu.edu.cn 

Gregory Kemp 

CSIRO Livestock Industry 

306 Carmody Road, 

St Lucia, QLD, Australia 

Gregory@lorikeet.biz 

ABSTRACT 

There are limits to miniaturization with current computer 

technologies. Information-processing capabilities of organic 

molecules such as DNA can be used in computers to replace 

digital switching modality. However, without the emergence of 

microfluidic devices, all operations in vitro would be user 

regulated. A more advanced model is where robotic and 

electronic regulation is combined with DNA computing allowing 

the majority of the operations within the test environment to be 

carried out automatically. Microfluidics offers the promise of a 

“lab on a chip” system. This can control pico liter scale volumes, 

with integrated support for operations such as mixing, storage, 

PCR, heating/cooling, cell lysis, electrophoresis, and others [1], 

[2]], [3]. Thus has emerged a vision for creating a hybrid DNA 

computer: that can use microfluidics for the control paths and 

biological primitives for computation (the Arithmetic Logical 

Units). This paper presents a proposed modularized DNA biochip 

computer that works in accordance with Von Neumann’s 

principles [4]. The biochips are divided into several modules, 

which have different functions. Thus, biochemical operations can 

be regulated in a step wise fusion. We then describe each module 

within the biochip and simulate how the classic Hamiltonian Path 

Problem would be solved in the proposed DNA computer. 

Study of Cache Placement for Time-shifted TV Cluster 

Using Genetic Algorithm 

Juchao Zhuo 

Dept. of Automation 


of China, Hefei, Anhui, 230027 

+86-0551-3620494

zjc5@mail.ustc.edu.cn 

Jun Li 




+86-0551-3602459 

ljun@ustc.edu.cn 

Gang Wu 




+86-0551-3601053 

wug@ustc.edu.cn 

ABSTRACT 

The designing of a streaming media system, especially Timeshifted 

TV cluster faces an optimization cache problem of 

deciding how to cache channels to multiple servers so that the 

blocking probability is minimized subject to memory capacity 

constraints. In this paper, we investigate the crucial problem by 

evaluating the blocking performance for a feasible assignment. A 

popularity-based random placement (PRP) scheme together with 

the genetic algorithm (GA) is developed to find an optimal or 

approximate optimal solution of the problem. The experiment 

results reveal that our proposed algorithm is efficient on 

improving the performance of Time-shifted TV cluster in terms of 

minimizing blocking probability. 

About the Dynamics of Essential Genetic Information: 

An Empirical Analysis for Selected GA-Variants 

Michael Affenzeller 

Department of Software 

Engineering 

Upper Austria University 

of Applied Sciences 

Softwarepark 11 

4232 Hagenberg, Austria 

michael@heuristiclab.com 

Andreas Beham 

Josef Ressel Centre for 

Heuristic Optimization 





abeham@heuristiclab.com 

Stefan Wagner 

Department of Software 

Engineering 





swagner@heuristiclab.com 

Stephan M. Winkler 

Department of Medical and 

Bioinformatics





swinkler@heuristiclab.com 

ABSTRACT 

This paper exemplarily points out how essential genetic in- 

formation evolves during the runs of selected GA-variants. 

The algorithmic enhancements to a standard genetic algo- 

rithm certify the survival of essential genetic information by 

supporting the survival of relevant alleles rather than the 

survival of above average chromosomes. This is achieved by 

de¯ning the survival probability of a new child chromosome 

depending on the child's ¯tness in comparison to the ¯tness 

values of its own parents. The main aim of this paper is 

to explain important properties of the discussed algorithm 

variants in a rather intuitive way. Aspects for meaningful 

and practically more relevant generalizations as well as more 

sophisticated experimental analyses are indicated. 

Analysis of Collision Probability in Vehicular Ad Hoc 

Networks 

Jianwei An 

ajw626 @ 126.com 

Xun Guo 

guoxun19 @ gmail.com 

Yang Yang 

yyang @ ustb.edu.cn 

Department of Communication Engineering, University of Science and Technology Beijing, Beijing, China 

ABSTRACT 

Vehicular Ad Hoc Network (VANET) is a new type of ad hoc 

network with the characteristics of highly dynamic topology, 

variable vehicle velocity and density. In the performance 

evaluation of VANETs, traditional collision probability model is 

not suitable because it poorly reflects these characteristics. 

However, collision probability is a vital ingredient for any 

performance evaluation in IEEE 802.11 system. In order to get 

more accurate results of VANETs’ performance evaluation, a new 

model to estimate the collision probability in VANETs is 

proposed in this paper, which integrated the traditional model 

with the characteristics of VANETs. The model shows that the 

collision probability in VANETs is no longer a constant value as 

in traditional model, but a function of the factors reflecting the 

characteristics of VANETs. It increases along with the increasing 

of vehicle velocity or vehicle density. Simulation results using 

Network Simulator 2 (ns-2) show the validity and accuracy of the 

proposed model. 

Pursuit Evasion Differential Game with Superior Evaders 

Ze-su Cai 

State Key Laboratory Robotics and 

System, Harbin Institute of 

Technology, 

Harbin 150001, China 

caizesu@hit.edu.cn 

Li-ning Sun

State Key Laboratory Robotics and 

System, Harbin Institute of 

Technology 


lnsun@hit.edu.cn 

Hai-bo Gao 

School of Mechatronics 

Engeneering, Harbin Institute of 

Technology, 


ABSTRACT 

In this paper, we consider a novel Hierarchical decomposition 

approach for multi-player pursuit evasion game (MPPEG) where 

some evaders’ capability are higher than those of all pursuers. 

Differently from standards MPPEGs where the environment and the 

location of evaders is unknown and a probabilistic map is built 

based on the pursuer onboard sensor. In this paper, we study the 

number of pursuers which necessitates for the capture condition and 

the time of all evaders have been captured. A novel Cooperative in 

the coalition formation is used for pursuer in their pursuit strategies 

deriving to 1) Avoid collision among objects, 2) Reduce the 

distance between each pursuer and the evader over the evolution of 

game; 3) Keep the pursuers’ formation around the evader invariant 

during the pursuit process and enclose the superior evader. The 

validity of our method is illustrated by two simulation examples. 

Optimal Feature Selection Algorithm Based 

on Quantum-Inspired Clone Genetic Strategy in Text 

Categorization 

Hao Chen 



Hunan, China 

+86 731 8617575 

xschenhao@gmail.com 

Beiji Zou 



Hunan, China 

+86 731 8877701 

bjzou@vip.163.com 

ABSTRACT 

Information overload is a serious issue in the modern society. As 

a powerful method to help people out of being “lost” in too much 

useless information, automatic text categorization is getting more 

and more important. Feature selection is the most important step 

in text categorization. To improve the performance of text 

categorization, we present a new text categorization method 

called quantum-inspired clone genetic algorithm (QCGA). The 

experimental results show that the QCGA algorithm is superior to 

other common methods. 

Evolutionary Multi-objective Optimization Algorithm Based 

on Global Crowding Diversity Maintenance Strategy 

Qiong Chen






Shengwu Xiong 

¤ 






Hongbing Liu 






ABSTRACT 

This paper presents an improved multi-objective evolution- 

ary algorithm based on global crowding diversity mainte- 

nance strategy and diversity initialization population strat- 

egy. In selection process, the global crowding strategy is 

applied to be a part of crowding operator which is used to 

select survival individuals. In the initialization process, one 

kind of diversity initialization population strategy is used 

to guarantee that the population can be widely spread at 

the beginning of evolutionary process. Numerical experi- 

ment results show that the proposed scheme improves diver- 

sity maintenance in evolutionary process. The results also 

demonstrate that the proposed algorithms can speed up the 

convergence and guide the solutions to be widely spread on 

the true Pareto optimal front. 

Analysis of Micro-Behavior and Bounded Rationality in 

Double Auction Markets Using Co-evolutionary GP 

Shu-Heng Chen 

National Chengchi University 

Taipei, Taiwan 

chchen@nccu.edu.tw 

Ren-Jie Zeng 

National Chengchi University 

Taipei, Taiwan 

93258038@nccu.edu.tw 

Tina Yu 

Memorial University 

of Newfoundland, Canada 

tinayu@cs.mun.ca 

ABSTRACT 

We investigate the dynamics of trader behaviors using a 

co-evolutionary genetic programming system to simulate a 

double-auction market. The objective of this study is two- 

fold. First, we seek to evaluate how, if any, the di_erence 

in trader rationality/intelligence inuences trading behav- 

ior. Second, besides rationality, we also analyze how, if any, 

the co-evolution between two learnable traders impacts their

trading behaviors. We have found that traders with di_erent 

degrees of rationality may exhibit di_erent behavior depend- 

ing on the type of market they are in. When the market has 

a pro_t zone to explore, the more intelligent trader demon- 

strate more intelligent behaviors. Also, when the market 

has two learnable buyers, their co-evolution produced more 

pro_table transactions than when there was only one learn- 

able buyer in the market. We have analyzed the learnable 

traders' strategies and found their behavior are very similar 

to humans in decision making. We will conduct human sub- 

ject experiments to validate these results in the near future. 

Categories and Subject Descriptors: H.4 [Information 

Systems Applications]: Miscellaneous 

General Terms: Economics, Experimentation, Algorithm. 

Keywords: Bounded rationality, co-evolution, double-auction. 

Bumblebees: A Multiagent Combinatorial Optimization 

Algorithm Inspired by Social Insect Behaviour 

Francesc Comellas 

Universitat Politècnica de Catalunya 

Dep. Matemàtica Aplicada IV - EPSC 

Avda. Canal Olimpic 15 

Castelldefels, Catalonia, Spain 

comellas@ma4.upc.edu 

Jesús Martínez-Navarro 

Universitat Politècnica de Catalunya 

Dep. Matemàtica Aplicada IV - EPSC 

Avda. Canal Olimpic s/n 

Castelldefels, Catalonia, Spain 

ABSTRACT 

This paper introduces a multiagent optimization algorithm 

inspired by the collective behavior of social insects. In this 

method, each agent encodes a possible solution of the problem 

to solve, and evolves in a way similar to real life insects. 

We test the algorithm on a classical difficult problem, the kcoloring 

of a graph, and we compare its performance in relation 

to a standard genetic algorithm and another multiagent 

system. The results show that this algorithm is faster and 

outperforms the other methods for a range of random graphs 

with different orders and densities. Moreover, the method 

is easy to adapt to solve different NP-complete problems. 

Categories and Subject Descriptors: I.2.11 [Distributed 

Artificial Intelligence]: Multiagent systems, G.1.6 [Optimization]: 

Miscellaneous, G.2.2 [Graph Theory]: Graph algorithms 

General Terms: Algorithms, Experimentation. 

Keywords: Multiagent System, Combinatorial optimization, 

Graph coloring, Adaptative complex systems. 

Research on an Orthogonal and Model Based 

Multi-objective Genetic Algorithm 

Guangming Dai1 Yanzhi Li 1, 2 Wei Zheng1 

1 School of Computer, China University of Geosciences, 

Wuhan City, Hubei Province, China 

2 Corresponding author 

gmdai@cug.edu.cn liyanzhi191031@163.com up2uwei@126.com 

ABSTRACT 

Against low efficiency of traditional multi-objective evolutionary

algorithms and poor utilization of Pareto-optimal solutions 

distribution regularity etc, in this paper, a new approach OMEA is 

proposed. It uses that distribution regularity to obtain good 

solutions, we also apply the orthogonal design to initialize 

population. Compared with SPEA2, NSGA-II and PAES, Pareto 

solutions by OMEA are closer to Pareto-optimal Front. The result of 

experiments shows a group of Pareto solutions with better 

convergence and diversity can be achieved, which gives strong 

supports to actual applications. 

Solving the Packing Problem of Rectangles with Improved 

Genetic Algorithm Based on Statistical Analysis 

Ding Genhong 




86-25-83786626 

dinggenhong@126.com 

Li Dan 




86-13913801376 

jisuan_hua@163.com 

Chen Leng 




86-15950524927 

qoossoop@163.com 

ABSTRACT 

The genetic algorithm and the surplus rectangle algorithm are 

used for solving the orthogonal packing problem of rectangles. 

Based on statistical analysis of rectangular packing problem, a 

comparable standard for judgment of a solution has been 

proposed, which is adopted in classification of the parent 

population. A surplus rectangle algorithm is introduced to decode 

the permutation of rectangles to the corresponding packing 

pattern uniquely. For different constructions, corresponding 

genetic operations have been designed. And then an improved 

genetic algorithm has been constructed. Several rectangles 

packing problems have been solved by using this improved 

algorithm and the optimum packing results have been achieved. 

This shows that the improved genetic algorithm is efficacious. 

Convergence Analysis of Gene Expression Programming 

Based on Maintaining Elitist 

Xin Du 

State-key Lab of Software 

Engineering, Wuhan University 

Wuhan, China 

Department of Information and 

Engineering, Shijiazhuang 

University of Economics 

Shijiazhuang, China

xindu79@126.com 

Lixin Ding 



Wuhan, China 

lxding@whu.edu.cn 

Chenwang Xie 



Wuhan, China 

chengwangxie@163.com 

Xing Xu 


Engineering, 


Wuhan, China 

whuxx84@yahoo.com.cn 

Shenwen Wang 

Shijiazhuang University of 

Economics 

Shijiazhuang, China 

wangshenwen@sina.com 

Li Chen 



Wuhan, China 

jasminecccc@sina.com 

ABSTRACT 

This paper analyzes the convergence of Gene Expression 

Programming based on maintaining elitist(ME-GEP).It is 

proved that ME-GEP algorithm will converge to the global 

optimal solution. The convergence speed of ME-GEP algorithm 

is estimated by the properties of transition matrices. 

The result hinges on four factors: population size, minimal 

transposition, mutation and selection probabilities. A category 

with the (minimum) three required fields 

An Improved Quantum Genetic Algorithm for Stochastic Job 

Shop Problem 

Jinwei Gu 


130 Meilong Road Xuhui 

District, Shanghai, China 

86-21-64252576 


Cuiwen Cao 

Bin Jiao 

Shanghai Dianji Univ. 690 

Jiangchuan Road Minhang 


86-21-54758615 





86-21-64252576 


Xingsheng Gu* 




86-21-64252576 

xsgu@ecust.edu.cn

ABSTRACT 

This paper considers the stochastic job shop scheduling problem with the objective of minimizing the expected 

value of makespan and the processing times of jobs being subject to independent normal distributions. In order to 

solve this problem, we devise an Improved Quantum Genetic Algorithm (IQGA) and develop a stochastic 

expected value model. Different from traditional genetic algorithms, IQGA employs the idea of quantum theory, 

devises a converting mechanism of quantum representation aiming at job shop code, and proposes a new rotation 

angle table as the update mechanism of populatio. In addition, three crossover operators and three mutation 

operators are compared in order to obtain the best combination to improve algorithm performance. Compared with 

standard Genetic Algorithm (GA), experimental results achieved by IQGA demonstrate its feasibility and 

effectiveness while dealing with the stochastic job shop problem. 

Descriptive Statistics of Non-Uniform Interval 

Symbolic 

Data 

Guo Jun-peng, 

School of Management, 

Tianjin University, 

P.R.China, 300072 

86-13602053107 

guojp@tju.edu.cn 

Li Wen-hua, 



P.R.China, 300072 

86-15900384566 

liwh@tju.edu.cn 

Gao Feng 



P.R.China, 300072 

86-13752355795 

gaofengtju@yahoo.com.cn 

ABSTRACT 

As a new kind of data mining method, symbolic data analysis 

(SDA) can not only decrease the computational complexity of 

huge data, but also master the property of the sample integrally by 

data package technology. Interval number is one of the most 

important types of symbolic data. Previous studies assumed each 

individual to be uniformly distributed within the interval, but the 

fact is not so. Non-uniform interval symbolic data is defined in

this paper, and the study is concentrated on their descriptive 

univariate statistics and bivariate statistics. On the basis of the 

study on empirical distribution function for non-uniform interval 

symbolic data, the calculation formula of mean and variance of 

non-uniform interval variables is achieved. Furthermore, 

covariance and correlation coefficient between two non-uniform 

interval variables are solved based on their empirical joint 

distribution function. Finally an example is given. 

The Optimum Method on Injection Molding 

Condition 

Based on RBF Network and Ant Colony 


Fengli Huang* 

School of Mechanical and Electrical 

Engineering, Jiaxing University 

WenChang road 355, Jiaxing 

Zhejiang, China 

+86-573-83643093, 314001 

Windon416@163.com 

Jinmei Gu 





+86-573-83643093, 314001 

Jmgu8037@163.com 

Jinhong Xu 





+86-573-83643602, 314001 

xujh@mail.zjxu.edu.cn 

ABSTRACT 

Aiming at the two principal; quality factors (warpage quantity and 

shrinkage rate) in injection molding process, the optimum method 

on injection molding condition based on RBF network and ant 

colony algorithm is provided. The definition and calculation

method of excellent degree are given first and then the optimum 

method of the approximate model based on radial basis neural 

network is given. In the case study of plastic injection of fruit 

plate, the range of molding condition and the design method of 

design variables based on excellent degree are given, then the 

approximate model is gotten by Hyper-Latin square experiment 

and RBF network, the optimum result is gotten by improved ant 

colony algorithm of continuous field. It shows that the optimum 

result of plastic injection parameters based on radial basis neural 

network response surface and ant colony algorithm is reliable, and 

has good practical meaning. 

A Genetic Algorithm for Solving Fourth-Party 

Logistics 

Routing Optimizing Problem with Fuzzy 

Duration Time 

Min Huang1,2 , Yan Cui1,2 , Xingwei Wang 2 , Hongyu Dong1,2 

1. Key Laboratory of Integrated Automation of Process Industry (Northeastern University), 

Ministry of Education 

2. College of Information Science and Engineering, Northeastern University, Shenyang, 

Liaoning, China, 110004 

mhuang@mail.neu.edu.cn 

ABSTRACT 

From the beginning of the 21st century, Fourth Party Logistics 

(4PL) has been attracting more and more attention in many fields. 

In this paper, a 4PL routing problem with fuzzy duration time is 

presented , and the fuzzy numbers is used to denote the 

uncertainty of the duration time. After a simple description of 4PL, 

a fuzzy programming for it is built and a crisp equivalent is 

derived by expected value. Then genetic algorithm is designed to 

solved the problem. Finally, an extensive computational analysis 

is presented and the numerical results show that which route 

should be selected in order to get minimum cost in the due date. 

Research on Flight Test Calibration Strategy 

Based on Data Fusion 

Hongwei Jiang

Chinese Flight Test Establishment. 

Xi’an,Shanxi,710089, China 

Tel:8613720583834 

kid_chao@yahoo.com.cn 

Zhaohui Yuan 

Northwestern Polytechnical University 

Xi’an,Shanxi,710072,China 

Tel:8613002993198 

yuanzhh@nwpu.edu.cn 

Yajuan Zhao 

Chinese Flight Test Establishment 

Xi’an,Shanxi,710089, China 

Tel:8613572257203 

zhaoyjuan@163.com 

ABSTRACT 

Research on environment conditions and other non-target 

parameters influence on the accurate and reliability of the 

micro -difference pressure test system and the influence of 

these non-target parameters are relationship each other. So 

this paper puts forward improving the integrated 

performance of the micro-difference pressure test system 

by using the data fusion technology and provide correct 

flight test data processing basis. 

Restoration of Coverage Blind Spots in Wireless 

Sensor 

Networks Based on Ant Colony Algorithm 

Lizhong Jin 

School of Information 

Science & Engineering 



jinlizhongneusoft@yahoo.co 

m.cn 

Jie Jia 




Shenyang 110004, China

jiajieneu@163.com 

Guiran Chang 

Computing Center 



chang@neu.edu.cn 

Xingwei Wang 





wangxw@mail.neu.edu.cn 

ABSTRACT 

Coverage control is one of the key problems of research and 

application of wireless sensor networks. In this paper, the 

coverage control problem for hybrid wireless sensor network 

consisting of both static and mobile sensors is investigated. A 

dynamic repair mechanism based on the ant colony algorithm is 

proposed for coverage blind spots found in the lifetime of wireless 

sensor networks. Simulation results show that the proposed 

method not only can make the nodes deployment more even, but 

also can improve the network quality of service, which verifies 

the effectiveness and feasibility of the restoration mechanism 

based on ant colony algorithm. 

Restoration of Coverage Blind Spots in Wireless 

Sensor 

Networks Based on Ant Colony Algorithm 

Lizhong Jin 





jinlizhongneusoft@yahoo.co 

m.cn 

Jie Jia 




Shenyang 110004, China

jiajieneu@163.com 

Guiran Chang 

Computing Center 



chang@neu.edu.cn 

Xingwei Wang 





wangxw@mail.neu.edu.cn 

ABSTRACT 

Coverage control is one of the key problems of research and 

application of wireless sensor networks. In this paper, the 

coverage control problem for hybrid wireless sensor network 

consisting of both static and mobile sensors is investigated. A 

dynamic repair mechanism based on the ant colony algorithm is 

proposed for coverage blind spots found in the lifetime of wireless 

sensor networks. Simulation results show that the proposed 

method not only can make the nodes deployment more even, but 

also can improve the network quality of service, which verifies 

the effectiveness and feasibility of the restoration mechanism 

based on ant colony algorithm. 

Finding All Global Solutions of Several Variables 

and Multimodal Function 

Xunguang Ju1 

Xuzhou Institute of Technology 

School of Information and Electrical 

Engineering 

86-13776798295 

jxg1966213@163.com 

Rong Bao1 



Engineering 

86-13852099979 

baorong@xzit.edu.cn 

Xiaogen Shao1 

Xuzhou Institute of Technology


Engineering 

86-13952296585 

shaoxg63@163.com 

Chengchun Han1 



Engineering 

han_chengchun@hotmail.com 

Liqing Xiao1 



Engineering 

86-13813282346 

doudouanddidi@sina.com 

Hongzhen Yu1 

China University of Mining and 

Technology 

College of Information and Electrical 

Engineering 

yuhoz@163.com 

ABSTRACT 

To improve Simple Genetic Algorithm convergence property s in 

the nonlinear and multimodal function of the optimization problem, 

constructing and applying the interval exclusion genetic algorithms 

(IEGA), the paper applied this hybrid algorithm to carrying on the 

global optimization problem of several variables and multimodal 

function in visual C++. The numerical experiment results showed 

that this algorithm is easy to be actualized, to have excellent 

performance. It is especially important for it ’ s speeding up 

upwards the convergence of 100% reliability and well solving the 

schema deception and premature convergence problem in Simple 

Genetic Algorithm. 

Representation and Recombination over Nonsingular 

Binary Matrices 

Yong-Hyuk Kim 

Department of Computer Science & Engineering 

Kwangwoon University 

Wolge-dong, Nowon-gu, Seoul, 139-701, Korea 

yhd_y@kw.ac.kr

Yourim Yoon 

School of Computer Science & Engineering 

Seoul National University 

Sillim-dong, Gwanak-gu, Seoul, 151-744, Korea 

yryoon@soar.snu.ac.kr 

ABSTRACT 

In this paper, we study nonsingular binary matrix space, 

GLn(Z2). The space is important in that it is used for the 

change of basis in binary encoding, which is the representation 

typically used in genetic algorithms. We analyze the 

properties of GLn(Z2) and discuss possible representation 

and recombination operators when used in evolutionary algorithms. 

Not only typical approaches but also ones using 

elementary matrices of linear algebra are presented. 

Symbolic Regression using Abstract Expression 

Grammars 

Michael F. Korns 

Freeman Investment Management 

1 Plum Hollow 

Henderson, Nevada 89052 

1 (702) 837 3498 

mkorns@korns.com 

ABSTRACT 

Abstract Expression Grammars have the potential to integrate 

Genetic Algorithms, Genetic Programming, Swarm Intelligence, 

and Differential Evolution into a seamlessly unified array of tools 

for use in symbolic regression. The features of abstract expression 

grammars are explored, examples of implementations are 

provided, and the beneficial effects of abstract expression 

grammars are tested with several published nonlinear regression 

problems. 

Synchronization Analysis and Control in Chaos 

System 

based on Complex Network 

Li Li 

Guilin University of Electronic Technology, Department

of Computer and Control 

Jinji Road No. 1, Guilin, Guangxi, China 

lili_top@163.com 

Feng Kong 

Guangxi University of Technology, Department of 

Electronic Information and Control Engineering 

Donghuan Road No. 268, Liuzhou, Guangxi, China 

gxkofe@163.com 

ABSTRACT 

For a certain kind of complex network, Lorenz chaos system is 

used to describe the state equation of nodes in network. By 

constructing a Lyapunov function, it is proved that this network 

model can achieve synchronization under the adaptive control 

scheme. The control strategy is simple, effective and easy for the 

engineering design in the future. The simulation results show the 

effectiveness of control scheme. 

Research on Multi-supplier Performance 

Measurement 

Based on Genetic Ant Colony Algorithm 

Li Xiaomei 


Tianjin University, Tianjin, CHINA 

+86-13820771588 

lxm@tju.edu.cn 

Mao Zhaofang 



+86-13302035658 

maozhaofang@tju.edu.cn 

Qi Ershi 



+86-22-27405100 

esqi@tju.edu.cn 

ABSTRACT 

With the growing maturation of the economical globalization and 

the fast progress of the IT industry, both the development of the 

global market and intellectual economy has overrun the national 

broad lines. However, the subsequent competition has also

ecoming fiercer and fiercer. Many enterprises have made more 

closely joint development with their partners, and built up “supply 

chain” with their partners to further expand supply-and-demand 

network. In the whole chain even the whole network Suppliers are 

upstream and key organizations of this chain and the net, selection 

of suppliers is the key for whole chain, and it plays important role 

for efficient operation of whole chain. Although many specialists 

have done research on multi-supplier selection and performance 

measurement system, it is still one of the most difficult problems for 

most manufacturing, but many subjective and objective issues exist 

during actual operation of supplier selection. In this paper, the 

improved genetic ant colony algorithm is used for research about 

selection of multi-supplier based on various relevant literatures 

about selection of suppliers at home and abroad. Via analysis for 

simulated examples, it is proven that this method is effective and 

feasible, and provides referential model and algorithm for selection 

of various types in supply chain. 

Categories and Subject Descriptors: K.6.4 [System Management]: 

Using the systematic Method to sole the management problem. 

General Terms: Algorithms, Management, Measurement, 

Performance, Design, Experimentation, Standardization, Theory, 

Verification. 

Keywords: Performance Measurement, Genetic Ant Colony 


Quantum-Inspired Evolutionary Clustering 


Based on Manifold Distance 

Yangyang Li 

yyli@xidian.edu.cn 

Hongzhu Shi 

shihongzhu1985@163.com 

Maoguo Gong 

gong@ieee.org 

Ronghua Shang 

rhshang@mail.xidian.edu.cn 

Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of 

China, 

Institute of Intelligent Information Processing, Xidian University 

Xi’an, 710071, China

ABSTRACT 

Based on the concepts and principles of quantum computing, a 

quantum-inspired evolutionary algorithm for data clustering 

(QECA) is proposed in this paper. And a novel distance 

measurement index called manifold distance is introduced. These 

attribute data are the main source of clustering problem, due to its 

complex distribution, most clustering algorithms available are 

only suitable for these types of characteristic data. In this study, a 

new algorithm which can deal with these data with manifold 

distribution is more effective. The main motives of using QECA 

consist in searching for appropriate cluster center so that a 

similarity metric of clusters are optimized more quickly and 

effectively. The superiority of QECA over fuzzy c-means (FCM) 

algorithm and immune evolutionary clustering algorithm (IECA) 

is extensively demonstrated in our experiments 

Image based Reconstruction using Hybrid 

Optimization 

of Simulated Annealing and Genetic Algorithm 

Cong Liu 

Shanghai University 

No.149 Yanchang Rd. 

Shanghai,20007,China 

+86-21-56334945 

lc82111@163.com 

Wangge Wan 




+86-21-56334945 


Youyong Wu 




+86-21-56334945 

wuyouyong1970@yahoo.com 

ABSTRACT 

This work deals with the problem of estimating depth information 

of 3-D surface from a pair of images. The proposed method relies

on Second-order Priors on the smoothness of 3D surface which 

cause intractable (non-submodular) optimization problems; we 

solved it by using the strategy of Hybrid Optimization of Simulated 

Annealing and Genetic Algorithm. Experimental results 

demonstrate the Second-order priors are a better model of typical 

scenes than first-order priors and the performance of the hybrid 

algorithm outperforms SA and GA alone. 

A Discrete Differential Evolution Algorithm for 

the Job 

Shop Scheduling Problem 

Fang Liu 

1.School of Computer 

Science and Technology 


Xi'an, China 

2.Key Laboratory 

of Intelligent Perception 

& Image Understanding 

of Ministry of Education 

of China, 

Institute of Intelligent 

Information Processing, 


Xi'an, China 

f63liu@163.com 

Yutao Qi 




Xi'an, China 





of China, 




Xi'an, China

qi_yutao@163.com 

Zhuchang Xia 




Xi'an, China 





of China, 




Xi'an, China 

hk8388@163.com 

Hongxia Hao 




Xi'an, China 





of China, 




Xi'an, China 

chilamhaohao@163.com 

ABSTRACT 

Differential Evolution (DE) Algorithm is a new evolutionary 

computation algorithm with rapid convergence rate. However, it 

does not perform well on dealing with job shop scheduling 

problems that have discrete decision variables. To remedy this, a 

Discrete Differential Evolution (DDE) Algorithm with special 

crossover and mutation operators is proposed to solve this 

problem. Under the skeleton of DE algorithm, The DDE 

algorithm inherits the advantage of rapid convergence rate. The 

experimental results on the well-known benchmark instances 

show the proposed algorithm is efficient in solving Job Shop 

Scheduling Problem.

Training Fuzzy Support Vector Machines by Using 

Boundary of Rough Set 

Hongbing Liu 






Shengwu Xiong 

¤ 






Qiong Chen 






ABSTRACT 

Support Vector Machines (SVMs) are statistical learning 

methods based on two-class problems and exist unclassi¯able 

regions when they are extended to multi-class problems. 

In order to reduce unclassi¯able regions, S. Abe and T. Inoue 

proposed the improved multi-class SVMs called Fuzzy 

Support Vector Machines (FSVMs) by which the unclassi¯able 

regions are reduced. In this paper, we train FSVMs by 

using the training data lying in the boundary of rough set. 

Firstly, the whole training set is divided into some equivalence 

classes by transforming all attribute values into discrete 

ones. Secondly, the lower approximation sets of the 

training data with the same categories are obtained by the 

formed equivalence classes. Thirdly, the boundary induced 

by the whole training set and the lower approximation sets 

is selected to form FSVMs. The experimental results on 

classic benchmark data sets show that the proposed learning 

machines can downsize the number of training data and 

achieve the higher predictions.

Stochastic Ranking Based Differential Evolution 


for Constrained Optimization Problem 

Ruochen Liu 

ruocheenliu@yahoo.com.cn 

Yong Li 

harness@126.com 

Wei Zhang 

javy198666@163.com 

Licheng Jiao 

Lchjiao@mail.xidan.edu.cn 

Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of 

China, 

Institute of Intelligent Information Processing, Xidian University 

Xi’an, 710071, China 

ABSTRACT 

Based on differential evolution and stochastic ranking strategy, a 

new differential evolution algorithm for constrained optimization 

problem is proposed in this paper. The proposed algorithm 

reserves sub-optimal solutions in the process of population 

evolution, which effectively enhances the diversity of the 

population. The experiment results on 13 well-known benchmark 

problems show that the proposed algorithm is capable of 

improving the search performance significantly in convergent 

speed and precision with respect to four other algorithms such as 

Evolutionary Algorithm based on Homomorphous Maps (EAHM), 

Artificial Immune Response Constrained Evolutionary Strategy 

(AIRCES), Constraint Handling Differential Evolution (CHDE), 

and Evolutionary Strategies based on Stochastic Ranking (ESSR). 

Segmentation of Multispectral Remote Sensing 

Images 

Based on Ant Colony Optimization Algorithm 

Shuo Liu 

The Institute of Remote Sensing 

Application, Chinese Academy of 

Sciences, Beijing, China 

P.O.Box 9718, Datun Road

+86-10-64862913 

liushuo@irsa.ac.cn 

Yan-you Qiao 




P.O.Box 9718, Datun Road 

+86-10-64862913 

yyqiao@irsa.ac.cn 

Qing-ke Wen 




P.O.Box 9718, Datun Road 

+86-10-64889205 

wenqingkeke@126.com 

ABSTRACT 

Segmentation of remote sensing image is not only a hot topic but a 

difficult technological field in remote sensing image processing as 

well. Recently, Ant Colony Optimization (ACO) algorithm has 

been introduced into image segmentation. But seldom study has 

been done in segmentation of multispectral remote sensing images 

based on Ant Colony Optimization Algorithm. In this paper, ACO 

algorithm is used in segmentation of multispectral remote sensing 

images. Three vectors of multispectral remote sensing images at 

each pixel site are extracted as eigenvectors, such as multispectrum 

gray values at one pixel site, mean gray values of 

neighborhood pixels in each band, and multi-spectrum gradient 

values at one pixel site. They reflect both value features and 

spatial features of remote sensing images. The combination of 

these three eigenvectors is used as the fuzzy cluster features. 

Furthermore, ACO Algorithm is used to optimize fuzzy clustering 

process. This method not only improves the segmentation result of 

multispectral remote sensing images, but also controls calculation 

amount effectively. Experiment and comparison results show that 

fuzzy clustering algorithm optimized by ACO is a preferable 

mothod for segmentation of multispectral remote sensing images. 

Hybrid Simulated Annealing Algorithm Based on 

Adaptive 

Cooling Schedule for TSP

Yi Liu 



University of Technology 

csliuyi@163.com 

Shengwu Xiong 

¤ 





Hongbing Liu 





ABSTRACT 

The traveling salesman problem(TSP) is one of the most 

notoriously intractable NP-complete optimization problems. 

Over the last 10 years, simulated annealing and tabu search 

have emerged as an e®ective algorithm for the TSP. However, 

the quality of solutions found by using tabu search 

approach depends on the initial solution and the iteration 

process of simulated annealing is slow. To overcome this 

problem and provide an e±cient methodology for the TSP, 

the heuristic search approach based on simulated annealing 

which combining tabu search strategy and two neighborhood 

perturbation factor is developed. The proposed 

hybrid algorithm is tested on standard benchmark sets and 

compared with the conventional simulated annealing algorithm. 

The computational results show that the proposed 

algorithm has signi¯cantly better convergence speed compared 

with conventional simulated annealing algorithm and 

can obtain high-quality solutions within reasonable computing 

times. 

A New Multimedia Information Data Mining 

Method 

Jin Longcun 


No. 149 Yanchang Road


+86-21-56331619 

longcunjin@163.com 

Wan Wanggen 


No. 149 Yanchang Road 


+86-21-56334945 


Cui Bin 




+86-21-56331619 

cuibin@shu.edu.cn 

Yu Xiaoqing 




+86-21-56334945 

yxq@staff.shu.edu.cn 

Xu Hongwei 




+86-21-56331619 

hongweixu@shu.edu.cn 

ABSTRACT 

In this paper, we proposed an annotated multimedia information 

data mining method. We present a Bayesian hierarchical framework 

model for mining objects in multimedia data. The Multimedia 

can switch between different shots, the unknown objects can 

leave or enter the scene at multiple times, and the background can 

be clustered. The proposed framework model consists of annotation 

part and Bayesian hierarchical mining part. This algorithm 

has several advantages over traditional distance-based agglomerative 

mining algorithms. Bayesian hierarchical hypothesis testing is 

used to decide which merges are advantageous and to output the 

recommended depth of the tree. The framework model can be 

interpreted as a novel fast bottom-up approximate inference method 

for a process mixture model. We describe procedures for 

learning the model hyperparameters, computing the predictive 

distribution, and extensions to the framework model. Experimental 

results on virtual reality multimedia data sets demonstrate useful

properties of the framework model. 

Hybrid EDA-based Optimal Attitude Control for a 

Spacecraft in a Class of Control Task 

Xiong Luo Zengqi Sun 

Department of Computer 




National Laboratory of Space 

Intelligent Control 


School of Information Engineering 

University of Science and Technology Beijing 


robertxiongluo@gmail.com 

szq-dcs@mail.tsinghua.edu.cn 

Xiang Zhang Laihong Hu Chao Wang 

Yangtze University, 

Jingzhou, Hubei 434023,China 

Department of Computer Science 




School of Information Engineering 

University of Science and 

Technology Beijing 


ABSTRACT 

In the practical situation, if failure of one of the actuators occurs, 

there exists the attitude control task of a rigid spacecraft using 

only two control torques supplied by momentum wheel actuators. 

Here, this class of control task for a rigid spacecraft is discussed. 

This nonlinear control problem can be converted to the 

nonholonomic motion planning optimization problem of a driftfree 

system. In order to improve the search efficiency of current 

optimization algorithms, the hybrid estimation of distribution 

algorithm (EDA) is presented by combing the idea of differential 

evolution strategy (DES). Then, the optimal attitude control task

for the spacecraft using two momentum wheel actuators is 

achieved. By comparing the proposed algorithm with existing 

genetic algorithm and evolutionary programming, the simulation 

results show the accuracy and efficiency of hybrid EDA. 

Emotional Speech Synthesis By XML File Using 

Interactive 

Genetic Algorithms 

Siliang Lv, Shangfei Wang, Xufa Wang 


Key Laboratory of Software in Computing and Communication in Anhui 

University of Science and Technology of China, Hefei Anhui, 230027 

lsliang@mail.ustc.edu.cn , sfwang@ustc.edu.cn , xfwang@ustc.edu.cn 

ABSTRACT 

As a technique that can ”let computer speak”, speech synthesis 

is drawing more and more attention. Today, much 

speech synthesis software can synthesize neutral speech naturally 

and flowingly. However, it is hard to make computers 

speak with ”emotion” as that in our daily life, because of 

the complexity of emotion model. Interactive Genetic Algorithms 

which can be acted self-organizingly, adaptively and 

self-learningly can just resolve the problem of difficulty in 

modeling emotional speech synthesis. As a result, this paper 

designs an emotional speech synthesis process, which 

adjusts the parameters (XML-tags) used to synthesize emotional 

speech dynamically, using interactive Genetic Algorithms, 

to optimize the quality of emotional speech. Also, 

the paper includes an evaluation experiment, which proves 

the feasibility of the algorithms. 

Computational Model Design and Performance 

Estimation 

in Registration Brake Control 

P.S. Pa 

Department of Digital Content Design, Graduate School 

of Toy and Game Design, National Taipei University of

Education 

No.134, Sec. 2, Heping E. Rd., Taipei City 106, 

Taiwan 

+886-2-27321104 

myhow@seed.net.tw 

S.C. Chang 

Department of Power Mechanical Engineering, 

Army Academy 

No.113, Sec. 4, Zhongshan E. Rd., Zhongli City, Taoyuan 

County 320, Taiwan 

+886-2-27321104 

Alexcsc2000@yahoo.com.tw 

ABSTRACT 

Electric motorcycles are applicable to both toys and real 

motorcycles, and also is a reference for constructing larger 

electrical vehicles. A design computational model of regenerative 

braking control of electric motorcycles and an experimental 

identification is presented to achieve regenerative current 

effectively. The purpose is to extend the driving distance of 

electric motorcycles by optimizing the brake regeneration energy. 

Based on the Time Ratio Control (TRC) method, two methods, 

one using the Hall sensor and the other using the optical encoder 

for feedback purposes, are proposed to achieve regenerative 

braking control. Simulation and experimental results show that 

both methods are effective in tracking the regenerative current 

command. By evaluating the simulation results, a simulator could 

provide valuable data to design and analyze prototypes of 

electrical vehicles. Therefore, rapid prototyping can be achieved 

to speed up the development of a new vehicle. 

Discussion on Convergence of a Fuzzy Adaptive 

Simulated Annealing Genetic Algorithm 

Peng Yonggang 

College of Electrical Engineering, 



pengyg@zju.edu.cn 

Luo Xiaoping* 


Hangzhou 

Zhejiang,310015,P.R.China


Wei Wei 

College of Electrical Engineering, 



wwei@zju.edu.cn 

ABSTRACT 

Due to shortcomings of genetic algorithm that its convergence 

speed is slow and it is often premature convergence, a new 

improved genetic algorithm---fuzzy adaptive simulated annealing 

genetic algorithm (FASAGA) is presented by integrating fuzzy 

inference, simulated annealing algorithm and adaptive mechanism. 

The strong Markovian property attributed to the population 

sequence was deduced by mathematical modeling. Then the 

convergence in probability of the fuzzy adaptive simulated 

annealing genetic algorithm was proved on the condition that the 

time tended to infinity. The results show that the methods are 

helpful for directing choice of better FASAGA parameters and 

improving the performance of the algorithm. 

A Hybrid Simulated Annealing Algorithm for 

Container 

Loading Problem 

Yu Peng 



China 

ypeng@cs.hku.hk 

Defu Zhang 



China 

dfzhang@xmu.edu.cn 

Francis Y.L. Chin 


The University of Hong Kong 

Hong Kong 

chin@cs.hku.hk 

ABSTRACT

This paper presents a hybrid simulated annealing algorithm for 

container loading problem with boxes of different sizes and single 

container for loading. A basic heuristic algorithm is introduced to 

generate feasible solution from a special structure called packing 

sequence. The hybrid algorithm uses basic heuristic to encode 

feasible packing solution as packing sequence, and searches in the 

encoding space to find an approximated optimal solution. The 

computational experiments on 700 weakly heterogeneous 

benchmark show that our algorithm outperforms all previous 

methods in average. 

A Dynamic Evolutionary Algorithm and Its 

Application 

in Automated Antenna Design 

Danping Yu , 

School of Computer Science, 

Research Center for Space 

China University of Geosciences 

Science & Technology 

Wuhan, 430074, China 

yudanping1001@163.com 

Sanyou Zeng 






Sanyou-zeng@263.net 

Song Gao, Zu Yan, Yulong Shi, 

Xianqiang Yang, Bo Xiao 






spectergs@gmail.com 

ABSTRACT 

Abstract: An X-band antenna has been designed for NASA’s 

Space Technology 5 (ST5) spacecraft by using genetic algorithm.

It had been deployed on schedule on March 22-June 30 2006 and 

became the first evolved hardware in space. It is known that 

antenna design is a complicated optimization problem with many 

constraints. In this paper, we take a different way to solve antenna 

problems: A dynamic evolutionary algorithm (DEA) is designed 

for solving general constrained optimization problems and well 

tested by a kit of benchmark constrained problems firstly. Then 

the algorithm is used to solve antenna design problems.Simulation 

results are quite promising. Our evolved antennas are quite 

competitive with NASA's. The algorithm will be applied in real 

antenna design in our future work. 

Keywords: Evolutionary algorithms, automated antenna design, 

constrained optimization, dynamic optimization 

Feedback-Control Modeling for Cellualr Response 

Mechanisms based on a Gene Regulatory 

Networks 

under Radiotherapy 

Jinpeng Qi*, Shihuang Shao, and Zhihai Rong 

College of Information Sciences and Technology, Donghua University, Shanghai 201620, China 

Engineering Research Center of Digitized Textile & Fashion Technology, Ministry of Education 

Donghua University, Shanghai 201620, P. R. China 

qipengkai@dhu.edu.cn, shshao@dhu.edu.cn, rongzhh@gmail.com, 

ABSTRACT 

In response to genome stresses, cell can trigger the self-defensive 

mechanisms by regulating the vital genes and their complicated 

signal pathways. To illustrate the celluar response in fighting 

against DNA damage under radiotherapy, a feedback-control 

model of P53 stress response networks is proposed at single cell 

level. The kinetics of Double Strand Breaks(DSBs) generation and 

repair, ARF and ATM activation, P53-MDM2 regulation, toxins 

degradation, as well as feedback-control to ion radiation (IR) dose 

are presented. 

Evolutionary Algorithm for Multi-objective

Optimization 

and its Application in Unmanned Flight Vehicle 

Trajectory 

Control 

Xu Qian 


School of Aerospace Engineering 

South Zhongguancun Street.5 

+8615711000124 

bj2003xuqian@gmail.com 

Tang Shengjing 




+8613911082906 

tangsj@bit.edu.cn 

Guo Jie 




+8613520828290 

maxmind@163.com 

ABSTRACT 

To make sure that unmanned flight vehicle safely landed on the 

ground, it is necessary to control its trajectory. By adopting proper 

control law and optimization, the vehicle can achieve a perfect 

landing, and resources can be most economically assigned. It is a 

multi-parameters and multi-objectives optimization (MPMO) 

problem. Two primary problems exist in traditional way: must 

simplify equation and easy to trap in constrained results. To solve 

these problems, an evolutionary algorithm using following 

strategies is adopted: 1. An interface for Simulink toolbox of 

Matlab, serving as core of the fitness function computing module; 

2. Norm based Regret Function serving as fitness function; 3. 

Adaptive crossover and mutation probability; 4. Elitist strategy. 

Result proves that the “Improved Genetic Algorithm (IGA)” has 

better ability in dealing with multi-objective optimization. Finally, 

the trajectory optimization problem of an unmanned flight vehicle 

is solved, and the result is satisfying.

Log-optimal Portfolio Models with Risk Control of 

VaR and 

CVaR Using Genetic Algorithms 

Sen Qin 

School of Science, Hangzhou Dianzi University 

Hangzhou, Zhejiang, 310018, P.R. China 

qinsen0425@gmail.com 

ABSTRACT 

Value-at-risk (VaR) and conditional value-at-risk (CVaR) 

have become two very popular measures of market risk during 

the last decade. Log-optimal portfolio problem with 

risk control of VaR and CVaR is put forward ¯rstly. Then, 

we propose the portfolio models with VaR and CVaR and 

prove the existence and uniqueness of the optimal solutions 

of these two models. We provide a newly genetic algorithm 

based on real-code strings of assets' returns to overcome the 

problem of local optima. Finally, an empirical study is carried 

out to illustrate the optimal solutions of the log-optimal 

portfolio models with VaR and CVaR. The numeric results 

indicate that the optimal portfolio of the log-optimal portfolio 

model with CVaR gives a balance between the investment 

risk and the return simultaneously, and is more e®ective 

than the corresponding portfolios of the VaR model and 

the mean-variance model. 

Selected Population Characteristics of 

Fine-grained Parallel Genetic Algorithms with 

Re-initialization 

Ivan Sekaj, Michal Oravec 

Institute of Control and Industrial Informatics 

Faculty of Electrical Engineering and Information Technology, Slovak University of Technology 

Ilkovičova 3, 812 19 Bratislava, Slovak Republic

ABSTRACT 

ivan.sekaj@stuba.sk, michal.oravec@stuba.sk 

A class of fine-grained parallel genetic algorithms (F-PGA) are analyzed and experimentally compared. Each node 

of the F-PGA represents a single individual. Selected topologies are proposed, which are using various parent 

selection and offspring selection methods. Also the influence of population re-initialization on the parallel genetic 

algorithm performance is analyzed and selected characteristics of evolutionary algorithm population are proposed. 

These characteristics represent such properties as relative number of modified genes and number of duplicate 

individuals in population. The results are demonstrated on examples with minimization of selected test functions. 

ABSTRACT 

Structural Damping Identification Using 

Analytic Wavelet Transformation 

Shen Jian-hong 

Institute of Civil Engineering 

Qingdao Technological University 

China Qingdao 266520 

sjhqwr@163.com 

Li Chun-xiang 

Department of Civil Engineering Shanghai University China Shanghai 200072 

li-chunxiang@vip.sina.com 

Li Jin-hua 

Department of Civil Engineering Shanghai University China Shanghai 200072 

jinhua.li.1981@gmail.com 

By applying the Analytic Wavelet Transform (AWT) based on Gabor wavelet function in conjunction with the 

well-known Random Decrement Technique (RDT), this paper analyzes the time-frequency resolution of Gabor 

wavelet and the process of identifying structural damping parameters. The method selecting the parameters of 

Gabor wavelet function and the formula determining the usable length of signal are thus proposed. Eventually, the 

efficiency of the present method is confirmed by applying it to a numerical simulation data of a three

degree-of-freedom (3DOF) structure with the closely natural frequencies and to ambient vibration measurements 

of a super high-rise building excited by wind. 

MILCS in Protein Structure Prediction with 

Default 

Hierarchies 

Robert E. Smith 


University College London 

London, United Kingdom 

+44 7771852565 

robert.elliott.smith@gmail.com 

Max K. Jiang 


University of London 

London, United Kingdom 

+44 7828761996 

m.jiang@cs.ucl.ac.uk 

ABSTRACT 

This paper studies the performance of a newly developed 

supervised Michigan-style learning classifier system (LCS), 

called MILCS, on protein structure prediction problems and our 

observation of its default hierarchies (DHs). We present 

experimental results, and contrast them to results from other 

machine learning systems, named XCS, UCS, GAssist, BioHEL, 

C4.5 and Naïve Bayes. We use our technique for visualizing 

explanatory power of the resulting rule sets and their hierarchical 

structure. Final comments include future directions for this 

research, including investigations in neural networks and other 

systems.

Maximum Margin Transfer Learning∗ 

Bai Su 

Institute of Software, Chinese Academy of 

Sciences 

Graduate University of Chinese Academy of 

Sciences 

P.O.Box 8718, Beijing, China 

subai@ios.ac.cn 

Yi-Dong Shen 

Institute of Software, Chinese Academy of 

Sciences 

P.O.Box 8718, Beijing, China 

ydshen@ios.ac.cn 

ABSTRACT 

To achieve good generalization in supervised learning, the 

training and testing examples are usually required to be 

drawn from the same source distribution. However, in many 

cases, this identical distribution assumption might be violated 

when a task from one new domain(target domain) 

comes, while there are only labeled data from a similar old 

domain(auxiliary domain). Labeling the new data can be 

costly and it would also be a waste to throw away all the 

old data. In this paper, we present a discriminative approach 

that utilizes the intrinsic geometry of input patterns 

revealed by unlabeled data points and derive a maximummargin 

formulation of unsupervised transfer learning. Two 

alternative solutions are proposed to solve the problem. Experimental 

results on many real data sets demonstrate the 

effectiveness and the potential of the proposed methods. 

Traffic Flow Forecasting Based on Multitask 

Ensemble 

Learning 

Shiliang Sun 

Department of Computer Science and Technology, East China Normal University 

500 Dongchuan Road, Shanghai 200241, China 

slsun@cs.ecnu.edu.cn 

ABSTRACT 

A new method for traffic flow forecasting based on multitask 

ensemble learning, which combines the advantages of

multitask learning and ensemble learning, is proposed. Traditional 

traffic flow forecasting methods are a single task 

learning mode, which may neglect potential rich information 

embedded in some related tasks. In contrast to this, multitask 

learning can integrate information from related tasks 

for effective induction. Recent developments also witness 

the potential of ensemble learning for traffic flow forecasting. 

This paper devises a new method named MTLBag, a 

combination of multitask learning and a famous ensemble 

learning method bagging, for traffic flow forecasting. 

Using a neural network predictor, this paper first empirically 

shows the superiority of multitask learning over single 

task learning for traffic flow forecasting. Experimental 

results also indicate that the performance of MTLBag is 

statistically significantly better than that of the multitask 

neural network predictor, and that MTLBag outperforms a 

state-of-the-art method Bayesian networks. 

Distributed Risk Management Model and 


for Virtual Enterprise with Private Information 

Xianli Sun1,2,3, Min Huang1,2, Xingwei Wang1, Fuqiang Lu1,2 

1. College of Information Science and Engineering, Northeastern University, Liaoning, 110004, 

China. 

2. Key Laboratory of Integrated Automation of Process Industry (Northeastern University), Ministry 

of 

Education, Liaoning, 110004, China 

3. Department of Information and Engineering, Shenyang Institute of Engineering, Liaoning, 

110136, China 

+86-24-83671469 

sxl710404@sina.com 

ABSTRACT 

For the desired profit and anticipated goal, the virtual enterprise 

(VE) must avoid the risk successfully. In view of its 

characteristics, such as the diversity of partners and distribution of 

cooperative regions, the idea of distributed decision-making 

(DDM) is applied to the management of the virtual enterprise’ 

risks, with a Organizational-DDM risk management model 

developed for those virtual enterprises which are in relation to 

enforced team and with private information. A taboo search 

algorithm is designed to solve the model. The computation results

of simulative examples show the effectiveness and feasibility of 

the model and algorithm 

Quantum and Biogeography based Optimization 

for a 

Class of Combinatorial Optimization 

Li-xiang Tan 

Dept. Electronic Science and Technology, University of 

Science and Technology of China 

Postbox 4, Hefei, China, 230027 

+86-551-3601802 

tlx@ustc.edu.cn 

Li Guo 

Dept. Electronic Science and Technology, University of 

Science and Technology of China 

Postbox 4, Hefei, China, 230027 

+86-551-3601802 

lguo@ustc.edu.cn 

ABSTRACT 

In this paper, an algorithm named Quantum and Biogeography 

based Optimization(QBO) is proposed to investigate the 

possibility of optimization by evolving multiple Quantum 

Probability Models(QPMs) via evolutionary strategies inspired by 

the mathematics of biogeography. In QBO, each QPM modeling 

an area in decision space represents a habitat, the whole 

population of QPMs evolve as an ecosystem with multiple 

habitats interacting. The migration and immigration mechanisms 

originally presented in Biogeography Based Optimization (BBO) 

[1] is introduced into QBO to implement the efficient information 

sharing among QPMs, which enhance the evolution of probability 

models towards the better status that can generate more better 

solutions. Experimental results on classical 0/1 knapsack 

problems of various scale show that the mechanisms in BBO are 

feasible to evolve multiple QPMs, and QBO is efficient for hard 

optimization problem. 

Using GA-ANN Algorithm to Predicate Coal 

Bump Energy

Yunliang Tan 

Key Laboratory of Mine Disaster 

Prevention and Control of Education 

Ministry 

579 Qian-wangang Road, 

Qingdao Economic and Technical 

Developing Zone,Qingdao, China 

86-532-86057017 

tylllp@163169.net 

Tongbin Zhao 

Natural Resources and 

Environmental School, Shandong 




Developing Zone, Qingdao, China 

86-532-86057946 

ztb@sdust.edu.cn 

Zhigang Zhao 

Natural Resources and Environmental 

School, Shandong University of 




Developing Zone, Qingdao, China 

86-532-86057946 

zzg19721008@yahoo.com.cn 

ABSTRACT 

A GA-ANN network was constructed for preidcating coal bump 

energy, based on the 300 training samples form simulated results 

with PFC2D software for different coal particle stiffness. It was 

tested that the average relative error of fitted-output value is only 

2.5%, the averagre relative error of generalized predicated output is 

only 8.4%.It is valuable for coal bump energy predication 

Modelling and Evolutionary Multi-objective 

Evaluation of 

Interdependencies and Work Processes in 

Airport 

Operations

Jiangjun Tang 

ITEE, UNSW@ADFA 

Canberra, Australia 

j.tang@adfa.edu.au 

Sameer Alam 



s.alam@adfa.edu.au 

Hussein Abbass 



h.abbass@adfa.edu.au 

Chris Lokan 



c.lokan@adfa.edu.au 

ABSTRACT 

An airport is a multi-stakeholders environment, with work 

processes and operations cutting across a number of organizations. 

Airport landside operations involve a variety of 

services and entities that interact and depend on each others. 

In this paper, we introduce the Landside Modelling and 

Analysis of Services (LAMAS) tool to simulate, analyze and 

evaluate the interdependencies of services in airport operations. 

A genetic algorithm is used to distribute resources 

among the different entities in an airport such that the level 

of service is maintained. The problem is modelled as a multiobjective 

constrained resource allocation problem with the 

objective functions being the maximization of quality of service 

while reducing the total cost. 

A GA-Based Automatic Pore Segmentation 


Hangjun Wang 

School of Information Information 

Science and Technology, ZheJiang 

Forestry University, 

Linan, China 311300 

whj@zjfc.edu.cn 

Hengnian Qi 

School of Information Information 

Science and Technology, ZheJiang



qihengnian@yahoo.com.cn 

Wenzhu Li 

School of Engineering, ZheJiang 



lwz@zjfc.edu.cn 

Guangqun Zhang 

School of Information Information Science and 

Technology, ZheJiang Forestry University, 


gloria@zjfc.edu.cn 

Paoping Wang 

College of Computer and Information Technology, 

Nanyang Normal University, 

Nanyang, China 311300 

thwbp@sohu.com 

ABSTRACT 

Pore feature is important for hardwood identification. But it’s 

difficult to segment pores from wood cross-section images since 

pore, fiber and longitudinal parenchyma in the image are similar 

in shapes but different only in size, and the different hardwood 

species varies in the size of pores. In order to segment pores 

automatically without parameters set manually, it is necessary to 

design an adaptive algorithm which may be applied for all kinds 

of hardwood cross-section images. In the paper, an adaptive 

method is proposed to evaluate the optimal threshold of closed 

region area for pore segmentation. The method sorts all closed 

regions according to the area and classifies closed regions into 

two classes with maximum between-class variance method. We 

implements the method based on genetic algorithm to overcome 

the drawback of being time-consuming. Experiment on images of 

hardwood species shows that the threshold obtained by the 

genetic algorithm is very close to but more efficient than the 

ordinary enumeration algorithm. Moreover, with the obtained 

threshold majority of pores can be extracted except for some very 

small ones.

Grouping Genetic Algorithm for Solving the Server Consolidation ...

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