Biogeography-based learning particle swarm optimization

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

    Research output: Contribution to journalArticle

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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 languageEnglish
    Number of pages23
    JournalSoft Computing
    Early online date8 Aug 2016
    DOIs
    Publication statusPublished - Dec 2017

    Fingerprint

    Particle swarm optimization (PSO)
    Particle Swarm Optimization
    Migration
    Learning Strategies
    Optimization
    Evolutionary algorithms
    Evolutionary Algorithms
    Update
    Learning
    Benchmark

    Keywords

    • particle swarm optimization
    • biogeography-based learning
    • exemplar generation
    • biogeography-based optimization
    • migration

    Cite this

    Chen, Xu ; Tianfield, Huaglory ; Mei, Congli ; Du, Wenli ; Liu, Guohai . / Biogeography-based learning particle swarm optimization. In: Soft Computing. 2017.
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    Biogeography-based learning particle swarm optimization. / Chen, Xu; Tianfield, Huaglory; Mei, Congli; Du, Wenli ; Liu, Guohai .

    In: Soft Computing, 12.2017.

    Research output: Contribution to journalArticle

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    AU - Tianfield, Huaglory

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    KW - exemplar generation

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