A Heterogeneous Particle Swarm

  • Luke Cartwright
  • Tim Hendtlass
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5865)


Almost all Particle Swarm Optimisation (PSO) algorithms use a number of identical, interchangeable particles that show the same behaviour throughout an optimisation. This paper describes a PSO algorithm in which the particles, while still identical, have two possible behaviours. Particles are not interchangeable as they make independent decisions when to change between the two possible behaviours. The difference between the two behaviours is that the attraction towards a particle’s personal best in one is changed in the other to repulsion from the personal best position. Results from experiments on three standard functions show that the introduction of repulsion enables the swarm to sequentially explore optima in problem space and enables it to outperform a conventional swarm with continuous attraction.


Particle Swarm Optimisation Collective Intelligence Optimisation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Luke Cartwright
    • 1
  • Tim Hendtlass
    • 1
  1. 1.Faculty of Information & Communication TechnologiesSwinburne University of TechnologyMelbourneAustralia

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