期刊
SOFT COMPUTING
卷 21, 期 24, 页码 7519-7541出版社
SPRINGER
DOI: 10.1007/s00500-016-2307-7
关键词
Particle swarm optimization; Biogeography-based learning; Exemplar generation; Biogeography-based optimization; Migration
资金
- Research Talents Startup Foundation of Jiangsu University [15JDG139]
- China Postdoctoral Science Foundation [2016M591783]
- Natural Science Foundation of Jiangsu Province [BK20160540]
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.
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