Biogeography-based learning particle swarm optimization

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

    Research output: Contribution to journalArticlepeer-review

    145 Citations (Scopus)
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    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
    Pages (from-to)7519-7541
    Number of pages23
    JournalSoft Computing
    Early online date8 Aug 2016
    Publication statusPublished - Dec 2017


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


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