A hybrid of particle swarm optimization and local search for multimodal functions

  • Authors:

  • Jin Qin;Yixin Yin;Xiaojuan Ban

  • Affiliations:

  • ,College of Information Engineering, University of Science & Technology Beijing, Beijing, China;College of Information Engineering, University of Science & Technology Beijing, Beijing, China;College of Information Engineering, University of Science & Technology Beijing, Beijing, China

  • Venue:
  • ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
  • Year:
  • 2010

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Abstract

The standard PSO has problems with consistently converging to good solutions, especially for multimodal functions The reason for PSO failing to find (global) optima is premature convergence Also, it has been shown in many empirical studies that PSO algorithms lack exploitation abilities In this paper, we propose a hybrid of particle swarm optimization and local search, in which a standard PSO algorithm incorporates a local search algorithm The standard PSO algorithm and the local search algorithm are devoted to exploration and exploitation of solution space, respectively Particle's current position is updated using update equation of standard PSO and then is refined by local search algorithm The introduction of a local search improves the capability of exploitation of local region of standard PSO and prevents from premature convergence The hybrid algorithm can locate multiple solutions without use of specific niching techniques The hybrid algorithm showed superior performance on a set of multimodal functions.