Soft Computing

, Volume 21, Issue 24, pp 7519–7541 | Cite as

Biogeography-based learning particle swarm optimization

  • Xu Chen
  • Huaglory Tianfield
  • Congli Mei
  • Wenli Du
  • Guohai Liu
Methodologies and Application

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

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

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Xu Chen
    • 1
  • Huaglory Tianfield
    • 2
  • Congli Mei
    • 1
  • Wenli Du
    • 3
  • Guohai Liu
    • 1
  1. 1.School of Electrical and Information EngineeringJiangsu UniversityZhenjiangChina
  2. 2.School of Engineering and Built EnvironmentGlasgow Caledonian UniversityGlasgowUK
  3. 3.Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of EducationEast China University of Science and TechnologyShanghaiChina

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