A hybrid genetic algorithm and particle swarm optimization for multimodal functions
A hybrid genetic algorithm and particle swarm optimization for multimodal functions
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DOI：
10.1016/j.asoc.2007.07.002
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发表时间：
2008-03-01
影响因子：
8.7
通讯作者：
Zahara, Erwie
中科院分区：
文献类型：
--
作者：
Kao, Yi-Tung;Zahara, Erwie
启发式优化为解决复杂的现实问题提供了一种强大而有效的方法。本研究的重点是一种混合方法相结合的启发式优化技术，遗传算法（GA）和粒子群优化（PSO），多峰函数的全局优化。被称为GA-PSO，这种混合技术结合了GA和PSO的概念，并创建一个新的一代，不仅在GA中发现的交叉和变异操作，但也通过PSO的机制的个人。各种实验研究的结果，使用一套17多模态测试功能从文献中已经证明了优越性的混合GA-PSO方法在其他四种搜索技术的解决方案的质量和收敛速度。(c)2007年由Elsevier B. V.出版。
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.