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.
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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.
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Zhi-jian, W., Zhi-long, T. & Li-shan, K. A parallel global-local mixed evolutionary algorithm for multimodal function optimization based on domain decomposition. Wuhan Univ. J. of Nat. Sci. 8, 253–258 (2003). https://doi.org/10.1007/BF02899489
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DOI: https://doi.org/10.1007/BF02899489