Biogeography-based learning particle swarm optimization

Xu Chen; Huaglory Tianfield; Congli Mei; Wenli  Du; Guohai  Liu

doi:10.1007/s00500-016-2307-7

Biogeography-based learning particle swarm optimization

Xu Chen, Huaglory Tianfield, Congli Mei, Wenli Du, Guohai Liu

Research output: Contribution to journal › Article › peer-review

203 Citations (Scopus)

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Abstract

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.

Original language	English
Pages (from-to)	7519-7541
Number of pages	23
Journal	Soft Computing
Volume	21
Early online date	8 Aug 2016
DOIs	https://doi.org/10.1007/s00500-016-2307-7
Publication status	Published - Dec 2017

Keywords

Biogeography-based learning
Biogeography-based optimization
Exemplar generation
Migration
Particle swarm optimization

ASJC Scopus subject areas

Software
Theoretical Computer Science
Geometry and Topology

Documents and Links

10.1007/s00500-016-2307-7

Chen, X. et al (2016) Biogeography-based learning particle swarm optimization
This is the accepted author manuscript of the article. The final publication is available at Springer via http://dx.doi.org/10.1007/s00500-016-2307-7
Author accepted manuscript, 762 KB

Cite this

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title = "Biogeography-based learning particle swarm optimization",

abstract = "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.",

keywords = "Biogeography-based learning, Biogeography-based optimization, Exemplar generation, Migration, Particle swarm optimization",

author = "Xu Chen and Huaglory Tianfield and Congli Mei and Wenli Du and Guohai Liu",

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AU - Chen, Xu

AU - Tianfield, Huaglory

AU - Mei, Congli

AU - Du, Wenli

AU - Liu, Guohai

N1 - AAM provided 31-1-17; 12m embargo Exception email in SAN ^Exception valid (library exception review, October 2018)

PY - 2017/12

Y1 - 2017/12

N2 - 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.

AB - 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.

KW - Biogeography-based learning

KW - Biogeography-based optimization

KW - Exemplar generation

KW - Migration

KW - Particle swarm optimization

U2 - 10.1007/s00500-016-2307-7

DO - 10.1007/s00500-016-2307-7

M3 - Article

SN - 1432-7643

VL - 21

SP - 7519

EP - 7541

JO - Soft Computing

JF - Soft Computing

ER -

Biogeography-based learning particle swarm optimization

Abstract

Keywords

ASJC Scopus subject areas

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