Particle swarm with speciation and adaptation in a dynamic environment

  • Authors:

  • Xiaodong Li;Jürgen Branke;Tim Blackwell

  • Affiliations:

  • RMIT University, Melbourne, Australia;University of Karlsruhe, Karlsruhe, Germany;Goldsmiths College University of London, London, United Kingdom

  • Venue:
  • Proceedings of the 8th annual conference on Genetic and evolutionary computation
  • Year:
  • 2006

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Abstract

This paper describes an extension to a speciation-based particle swarm optimizer (SPSO) to improve performance in dynamic environments. The improved SPSO has adopted several proven useful techniques. In particular, SPSO is shown to be able to adapt to a series of dynamic test cases with varying number of peaks (assuming maximization). Inspired by the concept of quantum swarms, this paper also proposes a particle diversification method that promotes particle diversity within each converged species. Our results over the moving peaks benchmark test functions suggest that SPSO incorporating this particle diversification method can greatly improve its adaptability hence optima tracking performance.