Development of a parallel optimization method based on genetic simulated annealing algorithm

Authors:
Z. G. Wang;Y. S. Wong;M. Rahman
Affiliations:
Department of Mechanical Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260, Singapore;Department of Mechanical Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260, Singapore;Department of Mechanical Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260, Singapore
Venue:
Parallel Computing
Year:
2005

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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 sub-populations, and in each sub-population the algorithm uses the local search ability of simulated annealing after crossover and mutation. The best individuals of each sub-population are migrated to neighboring ones after a certain number of epochs. An implementation of the algorithm is discussed and the performance is evaluated against a standard set of test functions. PGSA shows some remarkable improvement in comparison with the conventional parallel genetic algorithm and the breeder genetic algorithm (BGA).