期刊
PARALLEL COMPUTING
卷 31, 期 8-9, 页码 839-857出版社
ELSEVIER
DOI: 10.1016/j.parco.2005.03.006
关键词
genetic algorithm; simulated annealing; parallel genetic algorithm
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 subpopulation 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). (c) 2005 Elsevier B.V. All rights reserved.
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