Particle swarm optimizer with C-Pg mutation

  • Authors:

  • Guojiang Fu;Shaomei Wang;Mingjun Chen;Ning Li

  • Affiliations:

  • School of Computer, Wuhan University of Technology, Wuhan, China;School of Logistics, Wuhan University of Technology, Wuhan, China;School of Computer, Wuhan University of Technology, Wuhan, China;School of Computer, Wuhan University of Technology, Wuhan, China

  • Venue:
  • CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
  • Year:
  • 2005

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Abstract

This paper presents a modified PSO algorithm, called the PSO with C-Pg mutation, or PSOWC-Pg, the algorithm adopts C-Pg mutation, the idea is to replace global optimal point gBest with disturbing point C and gBest alternately in the original formulae, the probability of using C is R. There are two methods for selecting C: stochastic method and the worst fitness method. The stochastic method selects some particle’s current position x or pBest as C stochastically in each iteration loop, the worst fitness method selects the worst particle’s x or the pBest of some particle with the worst fitness value as C. So, when R is small enough, the distance between C and gBest will tend towards 0, particle swarm will converge slowly and irregularly. The results of experiments show that PSOWC-Pg exhibit excellent performance for test functions.