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Multi-swarm Particle Swarm Optimization with a Center Learning Strategy

  • Ben Niu
  • Huali Huang
  • Lijing Tan
  • Jane Jing Liang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7928)

Abstract

This paper proposes a new variant of particle swarm optimizers, called multi-swarm particle swarm optimization with a center learning strategy (MPSOCL). MPSOCL uses a center learning probability to select the center position or the prior best position found so far as the exemplar within each swarm. In MPSOCL, Each particle updates its velocity according to the experience of the best performing particle of its partner swarm and its own swarm or the center position of its own swarm. Experiments are conducted on five test functions to compare with some variants of the PSO. Comparative results on five benchmark functions demonstrate that MPSOCL achieves better performances in both the optimum achieved and convergence performance than other algorithms generally.

Keywords

multi-swarm particle swarm optimization center learning strategy particle swarm optimizer (PSO) 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ben Niu
    • 1
    • 2
    • 3
  • Huali Huang
    • 1
  • Lijing Tan
    • 4
  • Jane Jing Liang
    • 5
  1. 1.College of ManagementShenzhen UniversityShenzhenChina
  2. 2.Department of Industrial and System EngineeringThe Hong Kong Polytechnic UniversityHong Kong
  3. 3.Hefei Institute of Intelligent MachinesChinese Academy of SciencesHefeiChina
  4. 4.Management SchoolJinan UniversityGuangzhouChina
  5. 5.School of Electrical EngineeringZhengzhou UniversityZhengzhouChina

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