文章基本信息

标题：A novel animal migration algorithm for global numerical optimization
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作者：Luo, Qifang ; Ma, Mingzhi ; Zhou, Yongquan 等
期刊名称：Computer Science and Information Systems
印刷版ISSN：1820-0214
电子版ISSN：2406-1018
出版年度：2015
页码：41-41
DOI：10.2298/CSIS141229041L
出版社：ComSIS Consortium
摘要：Animal migration optimization (AMO) searches optimization solutions by migration process and updating process. In this paper, a novel migration process has been proposed to improve the exploration and exploitation ability of the animal migration optimization. Twenty-three typical benchmark test functions are applied to verify the effects of these improvements. The results show that the improved algorithm has faster convergence speed and higher convergence precision than the original animal migration optimization and other some intelligent optimization algorithms such as particle swarm optimization (PSO), cuckoo search (CS), firefly algorithm (FA), bat-inspired algorithm (BA) and artificial bee colony (ABC).
关键词：animal migration optimization algorithms; exploration and exploitation; functions optimization