design space pruning heuristics and global optimization method for ...

CHAPTER V
APPLICATION OF METHODOLOGY TO LARGER PROBLEMS
In this chapter, the methodology is applied in full to two larger problems where
the global optimum solution is unknown. The first problem is derived as a modified
version of the 3 rd Global Trajectory Optimization Competition, while the second problem
is modified version of the 2 nd Global Trajectory Optimization Competition. For each
problem, a number of known good solutions exists from the competition results, which
will serve as benchmarks to evaluate the effectiveness of the methodology. For both of
these problems, the goal is to find a suite of good solutions for subsequent analysis with
higher fidelity methods. Additionally, the methodology is applied to the full version of
the GTOC2 problem, subject to the time limitations of the competition, in order to
determine where the best solution found in that timeframe would have placed in the
competition.
For the larger problems, which will require greater computational resources, a
computer cluster is utilized to carry out the low-thrust trajectory optimizations. The
genetic algorithm is run on a computer cluster comprised of fifteen nodes. A Matlab
code runs on the master node of the cluster, which executes the genetic algorithm and
distributes the MALTO runs to each of the nodes. The master node contains two AMD
Opteron processors at 2.2 GHz each. MALTO then runs on the cluster nodes. A Fortran
script creates the input files required for each of the MALTO runs, based on a batch
script sent to each node by Matlab. Seven of these nodes contain two AMD Opteron
processors with 2.2 GHz each and 5 GB of RAM. The remaining eight nodes contain
two dual core AMD Opteron processors with 2.4 GHz each and 12 GB of RAM. The
operating system on the computer cluster is Ubuntu 8.04 LTS.
Because of limitations in the Matlab Distributed Computing Server, each
generation of the genetic algorithm was manually distributed to the nodes – e.g., if sixty
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