Biogeography-based learning particle swarm optimization

被引：260

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

来源：

SOFT COMPUTING | 2017年 / 21卷 / 24期

基金：

中国博士后科学基金;

关键词：

Particle swarm optimization; Biogeography-based learning; Exemplar generation; Biogeography-based optimization; Migration; ALGORITHM; EVOLUTION;

D O I：

10.1007/s00500-016-2307-7

中图分类号：

TP18 [人工智能理论];

学科分类号：

140502 [人工智能];

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.

引用

页码：7519 / 7541

页数：23

共 42 条

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