Dispersed particle swarm optimization

  • Authors:

  • Xingjuan Cai;Zhihua Cui;Jianchao Zeng;Ying Tan

  • Affiliations:

  • Division of System Simulation and Computer Application, Taiyuan University of Science and Technology, Taiyuan 030024, P.R. China;State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, P.R. China and Division of System Simulation and Computer Application, Taiyuan University of Sc ...;Division of System Simulation and Computer Application, Taiyuan University of Science and Technology, Taiyuan 030024, P.R. China;Division of System Simulation and Computer Application, Taiyuan University of Science and Technology, Taiyuan 030024, P.R. China

  • Venue:
  • Information Processing Letters
  • Year:
  • 2008

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Abstract

In particle swarm optimization (PSO) literatures, the published social coefficient settings are all centralized control manner aiming to increase the search density around the swarm memory. However, few concerns the useful information inside the particles' memories. Thus, to improve the convergence speed, we propose a new setting about social coefficient by introducing an explicit selection pressure, in which each particle decides its search direction toward the personal memory or swarm memory. Due to different adaptation, this setting adopts a dispersed manner associated with its adaptive ability. Furthermore, a mutation strategy is designed to avoid premature convergence. Simulation results show the proposed strategy is effective and efficient.