文献类型：期刊文献

英文题名：Blended biogeography-based optimization for constrained optimization

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：3

起止页码：517

外文期刊名：ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

收录：SCI-EXPANDED(收录号：WOS:000288773200010)、、EI(收录号：20110913700199)、Scopus(收录号：2-s2.0-79951956537)、WOS

基金：This work was partially 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：Evolutionary algorithm; Biogeography-based optimization; Constrained optimization

外文摘要：Biogeography-based optimization (BBO) is a new evolutionary optimization method that is based on the science of biogeography. We propose two extensions to BBO. First, we propose a blended migration operator. Benchmark results show that blended BBO outperforms standard BBO. Second, we employ blended BBO to solve constrained optimization problems. Constraints are handled by modifying the BBO immigration and emigration procedures. The approach that we use does not require any additional tuning parameters beyond those that are required for unconstrained problems. The constrained blended BBO algorithm is compared with solutions based on a stud genetic algorithm (SGA) and standard particle swarm optimization 2007 (SPSO 07). The numerical results demonstrate that constrained blended BBO outperforms SGA and performs similarly to SPSO 07 for constrained single-objective optimization problems. (C) 2010 Elsevier Ltd. All rights reserved.