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Abstracts - Dipartimento di Elettronica Applicata

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Meta 2010 & FEM 2010 – Rome, 13-15 December 2010<br />

Optimization of Multistage Depressed Collectors by<br />

using FEM and METEO<br />

Salvatore Coco (1) , Antonino Laudani (1) , Giuseppe Pulcini (2) , Francesco Riganti<br />

Fulginei (2) , Alessandro Salvini (2)<br />

(1) University of Catania, DIEES Catania, Italy – e-mail: alaudani@<strong>di</strong>ees.unict.it<br />

(2) Roma Tre University, Department of Applied Electronics - Roma, Italy<br />

Specialized 3-D simulators are required by TWT multistage depressed collector<br />

(MDC) designers to help them to analyze and test new and arbitrarily shaped<br />

geometries for high efficiency TWTs. To perform such a task a Finite Element (FE)<br />

approach can be pursued, since it allows a very flexible meshing and gives the<br />

possibility of using irregular meshes to fit properly the MDC’s geometry; in such a<br />

way complex geometries can be accurately simulated [1]. Unfortunately, the use of<br />

optimization techniques in the design process of these devices is rarely used [2],<br />

because of the high number of parameters and the high computational cost of<br />

efficiency evaluation (the fitness function). In this paper the authors present the<br />

application of a novel metaheuristics technique called METEO (Metric-Topologicalevolutionary-Optimization)<br />

[3], to optimize the performance of multistage collectors,<br />

simulated by means of Finite element collector and electron gun simulator<br />

COLLGUN [4]. METEO [3] is a hybrid algorithm composed by three <strong>di</strong>fferent<br />

heuristics: FSO (Flock of Starlings Optimization) [5], PSO (Particle Swarm<br />

Optimization), and BCA (Bacterial Chemotaxis Algorithm); it performs the<br />

optimization using both the topological as the metric rules. In fact the mentioned<br />

heuristics own a <strong>di</strong>fferent performance taken alone, in particular FSO presents a high<br />

degree of exploration, and a good capability to escape from local minima, whilst PSO,<br />

and BCA own a good proprieties of convergence. Besides, they offer a natural parallel<br />

implementation that allows spee<strong>di</strong>ng up the whole process of optimization. This<br />

characteristic can be useful exploited in order to obtain the desired target with an<br />

acceptable computational cost.<br />

References<br />

[1] S. Coco, F. Emma, A. Laudani, S. Pulvirenti, M. Sergi, “COCA: A Novel 3-D FE Simulator for<br />

the Design of TWTs Multistage Collectors”, IEEE trans. on electron devices, 48 , 24-31, 2001.<br />

[2] T. K. Ghosh, R.G. Carter, “Optimization of Multistage Depressed Collectors”, IEEE trans. on<br />

electron devices, 54 , 2031-2039, 2007.<br />

[3] G. Pulcini, F. Riganti Fulginei, A. Salvini, “Metric-Topological-Evolutionary Optimization”,<br />

OIPE2010 Procee<strong>di</strong>ngs, Sofia - Bulgaria, Sept. 14-18 2010, pp. 3-4.<br />

[4] S. Coco, S. Corsaro, A. Laudani, G. Pollicino, R. Dionisio, and R. Martorana, “COLLGUN: A 3D<br />

FE simulator for the design of TWT's electron guns and multistage collectors”, Scientific<br />

Computing in Electrical Engineering, Mathematics in Industry, Springer-Verlag, 9, 175-180, 2006<br />

[5] F. R. Fulginei, A. Salvini, “Model identification by the Flock-of-Starlings Optimization”, Int.<br />

Journal of applied Electromagnetics and Mechanics, vol. 30, n. 3-4, pp. 321-331, 2009.<br />

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