The landscape adaptive particle swarm optimizer

  • Authors:

  • Jin Yisu;Joshua Knowles;Lu Hongmei;Liang Yizeng;Douglas B. Kell

  • Affiliations:

  • College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR China;School of Chemistry, University of Manchester, Manchester M60 1QD, UK;College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR China;College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR China;School of Chemistry, University of Manchester, Manchester M60 1QD, UK

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2008

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Abstract

Several modified particle swarm optimizers are proposed in this paper. In DVPSO, a distribution vector is used in the update of velocity. This vector is adjusted automatically according to the distribution of particles in each dimension. In COPSO, the probabilistic use of a 'crossing over' update is introduced to escape from local minima. The landscape adaptive particle swarm optimizer (LAPSO) combines these two schemes with the aim of achieving more robust and efficient search. Empirical performance comparisons between these new modified PSO methods, and also the inertia weight PSO (IFPSO), the constriction factor PSO (CFPSO) and a covariance matrix adaptation evolution strategy (CMAES) are presented on several benchmark problems. All the experimental results show that LAPSO is an efficient method to escape from convergence to local optima and approaches the global optimum rapidly on the problems used.