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A Particle Swarm Optimization Using Local Stochastic Search for Continuous Optimization

  • Jianli Ding
  • Jin Liu
  • Yun Wang
  • Wensheng Zhang
  • Wenyong Dong
Part of the Communications in Computer and Information Science book series (CCIS, volume 304)

Abstract

The particle swarm optimizer (PSO) is a swarm intelligence based heuristic optimization technique that can be applied to a wide range of problems. After analyzing the dynamics of tranditioal PSO, this paper presents a new PSO variant based on local stochastic search strategy (LSSPSO) for performance enhancement. This is inspired by a social phenomenon that everyone wants to first exceed the nearest superior and then all superior. Specifically, LSSPSO adopts a local stochastic search to adjust inertia weight in terms of keeping a balance between the diversity and the convergence speed, aiming to improve the performance of tranditioal PSO. Experiments conducted on unimodal and multimodal test functions demonstrate the effectiveness of LSSPSO in solving multiple benchmark problems as compared to several other PSO variants.

Keywords

particle swarm optimization continuous optimization local stochastic search 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jianli Ding
    • 1
  • Jin Liu
    • 1
    • 2
  • Yun Wang
    • 3
  • Wensheng Zhang
    • 2
  • Wenyong Dong
    • 1
    • 2
  1. 1.Computer SchoolWuhan UniversityWuhanPR China
  2. 2.State Key Lab. of Management and Control for Complex Systems, Institute of AutomationChinese Academy of ScienceBeijingPR China
  3. 3.Computer Science and Information SystemsBradley UniversityUSA

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