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Publication Detail
Evolution of Forces for Particle Swarm Optimisation using Genetic Programming
  • Publication Type:
    Conference
  • Authors:
    Chio CD, Poli R, Langdon WB
  • Publication date:
    2005
  • Published proceedings:
    CEC-2005
  • Notes:
    Rejected keywords: genetic algorithms, genetic programming, PSO notes:
Abstract
Particle Swarm Optimisation (PSO) uses a population of interacting particles that, controlled by physical forces, fly over the fitness landscape searching for an optimal solution. We extend our previous research on evolving these forces by considering additional ingredients, such as the velocity of the neighbourhood best and time, and different neighbourhood topologies, namely the global and ring ones. We test the evolved extended PSOs (XPSOs) on various classes of benchmark problems. We show that evolutionary computation (and in particular genetic programming, GP) can automatically generate new PSO algorithms that outperform standard PSOs designed by people as well as some previously evolved ones.
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