期刊
APPLIED SOFT COMPUTING
卷 8, 期 2, 页码 849-857出版社
ELSEVIER
DOI: 10.1016/j.asoc.2007.07.002
关键词
heuristic optimization; multimodal functions; genetic algorithms; particle swarm optimization
Heuristic optimization provides a robust and efficient approach for solving complex real-world problems. The focus of this research is on a hybrid method combining two heuristic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO), for the global optimization of multimodal functions. Denoted as GA-PSO, this hybrid technique incorporates concepts from GA and PSO and creates individuals in a new generation not only by crossover and mutation operations as found in GA but also by mechanisms of PSO. The results of various experimental studies using a suite of 17 multimodal test functions taken from the literature have demonstrated the superiority of the hybrid GA-PSO approach over the other four search techniques in terms of solution quality and convergence rates. (c) 2007 Published by Elsevier B.V.
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