文献类型：期刊文献

英文题名：Analysis of migration models of biogeography-based optimization using Markov theory

作者：Ma, Haiping[1];Simon, Dan[2]

机构：[1]Shaoxing Univ, Dept Elect Engn, Shaoxing, Zhejiang, Peoples R China;[2]Cleveland State Univ, Dept Elect & Comp Engn, Cleveland, OH 44115 USA

年份：2011

卷号：24

期号：6

起止页码：1052

外文期刊名：ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

收录：SCI-EXPANDED(收录号：WOS:000293202200013)、、EI(收录号：20112814129700)、Scopus(收录号：2-s2.0-79960013327)、WOS

基金：This material is based upon work supported by the Zhejiang Provincial Natural Science Foundation of China under Grant no. Y1090866, and by Grant 0826124 in the CMMI Division of the Engineering Directorate of the National Science Foundation.

语种：英文

外文关键词：Biogeography-based optimization; Evolutionary algorithms; Migration model; Markov chain; Population distribution

外文摘要：Biogeography-based optimization (BBO) is a new evolutionary algorithm inspired by biogeography, which involves the study of the migration of biological species between habitats. Previous work has shown that various migration models of BBO result in significant changes in performance. Sinusoidal migration models have been shown to provide the best performance so far. Motivated by biogeography theory and previous results, in this paper a generalized sinusoidal migration model curve is proposed. A previously derived BBO Markov model is used to analyze the effect of migration models on optimization performance, and new theoretical results which are confirmed with simulation results are obtained. The results show that the generalized sinusoidal migration model is significantly better than other models for simple but representative problems, including a unimodal one-max problem, a multimodal problem, and a deceptive problem. In addition, performance comparison is further investigated through 23 benchmark functions with a wide range of dimensions and diverse complexities, to verify the superiority of the generalized sinusoidal migration model. (C) 2011 Elsevier Ltd. All rights reserved.