An Empirical Comparison of Particle Swarm and Predator Prey Optimisation

  • Authors:
  • Arlindo Silva;Ana Neves;Ernesto Costa

  • Affiliations:
  • -;-;-

  • Venue:
  • AICS '02 Proceedings of the 13th Irish International Conference on Artificial Intelligence and Cognitive Science
  • Year:
  • 2002

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Abstract

In this paper we present and discuss the results of experimentally comparing the performance of several variants of the standard swarm particle optimiser and a new approach to swarm based optimisation. The new algorithm, which we call predator prey optimiser, combines the ideas of particle swarm optimisation with a predator prey inspired strategy, which is used to maintain diversity in the swarm and preventing premature convergence to local suboptima. This algorithm and the most common variants of the particle swarm optimisers are tested in a set of multimodal functions commonly used as benchmark optimisation problems in evolutionary computation.