June 26 - 30, 2004
Saturday to Wednesday
Seattle, Washington, USA

 

 

Session:

LBP - Late Breaking Papers

Title:

Development of the parallel optimization method based on genetic simulated annealing

   

Authors:

Z.G Wang
Y.S. Wong
M. Rahman

   

Abstract:

This paper presents a parallel genetic simulated annealing (PGSA) algorithm that has been developed and applied to optimize continuous problems. In PGSA, the entire population is divided into subpopulations, and in each subpopulation the algorithm uses the local search ability of simulated annealing after crossover and mutation. The best individuals of each subpopulation are migrated to neighboring ones after certain number of epochs. An implementation of the algorithm is discussed and the performance evaluation is made against a standard set of test functions. PGSA shows some remarkable improvement in comparison with the conventional simulated annealing, parallel genetic algorithm.

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