文献类型：期刊文献

英文题名：Biogeography-based learning particle swarm optimization

作者：Chen, Xu[1];Tianfield, Huaglory[2];Mei, Congli[1];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]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2017

卷号：21

期号：24

起止页码：7519

外文期刊名：SOFT COMPUTING

收录：;EI(收录号：20163302707036);WOS:【SCI-EXPANDED(收录号:WOS:000414961100020)】；

基金：This work was partly supported by the Research Talents Startup Foundation of Jiangsu University (Grant No. 15JDG139), the China Postdoctoral Science Foundation (Grant No. 2016M591783), and the Natural Science Foundation of Jiangsu Province (Grant No. BK20160540). The authors would like to especially thank Dr. Wenyin Gong for his helpful comments on work of this paper. The authors would appreciate the scientific efforts of Dr. N. Hansen, Dr. C. Garcia-Martinez, Dr. J. Zhang, and Dr. Y. Jin in making available the source codes of CMAES, GL-25, JADE, and SL-PSO, and Dr. P. N. Suganthan for providing the source codes of CLPSO, DMSPSO, and SaDE.

语种：英文

外文关键词：Particle swarm optimization; Biogeography-based learning; Exemplar generation; Biogeography-based optimization; Migration

摘要：This paper explores biogeography-based learning particle swarm optimization (BLPSO). Specifically, based on migration of biogeography-based optimization (BBO), a new biogeography-based learning strategy is proposed for particle swarm optimization (PSO), whereby each particle updates itself by using the combination of its own personal best position and personal best positions of all other particles through the BBO migration. The proposed BLPSO is thoroughly evaluated on 30 benchmark functions from CEC 2014. The results are very promising, as BLPSO outperforms five well-established PSO variants and several other representative evolutionary algorithms.