Particle swarm optimization with composite particles in dynamic environments

  • Authors:

  • Lili Liu;Shengxiang Yang;Dingwei Wang

  • Affiliations:

  • College of Information Science and Engineering, Northeastern University, Shenyang, China and Key Laboratory of Integrated Automation of Process Industry, Northeastern University, Ministry of Educa ...;Department of Computer Science, University of Leicester, Leicester, UK;College of Information Science and Engineering, Northeastern University, Shenyang, China and Key Laboratory of Integrated Automation of Process Industry, Northeastern University, Ministry of Educa ...

  • Venue:
  • IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
  • Year:
  • 2010

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Abstract

In recent years, there has been a growing interest in the study of particle swarm optimization (PSO) in dynamic environments. This paper presents a new PSO model, called PSO with composite particles (PSO-CP), to address dynamic optimization problems. PSO-CP partitions the swarm into a set of composite particles based on their similarity using a "worst first" principle. Inspired by the composite particle phenomenon in physics, the elementary members in each composite particle interact via a velocity-anisotropic reflection scheme to integrate valuable information for effectively and rapidly finding the promising optima in the search space. Each composite particle maintains the diversity by a scattering operator. In addition, an integral movement strategy is introduced to promote the swarm diversity. Experiments on a typical dynamic test benchmark problem provide a guideline for setting the involved parameters and show that PSO-CP is efficient in comparison with several state-of-the-art PSO algorithms for dynamic optimization problems.