文献类型：期刊文献

英文题名：Biogeography-based optimization with covariance matrix based migration

作者：Chen, Xu[1];Tianfield, Huaglory[2];Du, Wenli[3];Liu, Guohai[1]

机构：[1]Jiangsu Univ, Sch Elect & Informat Engn, Zhenjiang 212013, Peoples R China;[2]Glasgow Caledonian Univ, Sch Engn & Built Environm, Glasgow G4 0BA, Lanark, Scotland;[3]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2016

卷号：45

起止页码：71

外文期刊名：APPLIED SOFT COMPUTING

收录：;EI(收录号：20161802335192);WOS:【SCI-EXPANDED(收录号:WOS:000377411000006)】；

基金：The first author would like to acknowledge the supports by the Research Talents Startup Foundation of Jiangsu University (Grant No. 15JDG139), and the Priority Academic Program Development of Jiangsu Higher Education Institutions.

语种：英文

外文关键词：Biogeography-based optimization; Covariance matrix based migration; Global numeric optimization; Evolutionary algorithm; Rotational invariance

摘要：Biogeography-based optimization (BBO) is a new evolutionary algorithm. The major problem of basic BBO is that its migration operator is rotationally variant, which leaves BBO performing poorly in non separable problems. To overcome this drawback of BBO, in this paper, we propose the covariance matrix based migration (CMM) to relieve BBO's dependence upon the coordinate system so that BBO's rotational invariance is enhanced. By embedding the CMM into BBO, we put forward a new BBO approach, namely biogeography-based optimization with covariance matrix based migration, called CMM-BBO. Specifically, CMM-BBO algorithms are developed by the CMM operator being randomly combined with the original migration in various existing BBO variants. Numeric simulations on 37 benchmark functions show that our CMM-BBO approach effectively improves the performance of the existing BBO algorithms. (C) 2016 Elsevier B.V. All rights reserved.