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A parallel global-local mixed evolutionary algorithm for multimodal function optimization based on domain decomposition
Authors:Wu?Zhi-jian  author-information"  >  author-information__contact u-icon-before"  >  mailto:zjwu@public.wh.hb.cn"   title="  zjwu@public.wh.hb.cn"   itemprop="  email"   data-track="  click"   data-track-action="  Email author"   data-track-label="  "  >Email author,Tang?Zhi-long,Kang?Li-shan
Affiliation:(1) State Key Laboratory of Software Engineering, Wuhan University, 430072 Wuhan, Hubei, China
Abstract:This paper presents a parallel two-level evolutionary algorithm based on domain decomposition for solving function optimization problem containing multiple solutions. By combining the characteristics of the global search and local search in each sub-domain, the former enables individual to draw closer to each optima and keeps the diversity of individuals, while the latter selects local optimal solutions known as latent solutions in sub-domain. In the end, by selecting the global optimal solutions from latent solutions in each sub-domain, we can discover all the optimal solutions easily and quickly. Foundation item: Supported by the National Natural Science Foundation of China (60133010,60073043,70071042) Biography: Wu Zhi-jian(1963-), male, Associate professor, research direction: parallel computing, evolutionary computation.
Keywords:function optimization  GT algorithm  GLME algorithm  evolutionary algorithm  domain decomposition
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