Complexity International       /vol12/msid17/ © Copyright 2008     
Volume 12 Received: 
Accepted: 
November 2004
December 2004



Discrete Evaluation and the Particle Swarm Algorithm

Tim Hendtlass, Tom Rodgers

Abstract
     We propose that the optimal performance of the PSO algorithm should differ from that of the real life creatures on which PSO is modelled. If a bird finds a good food source, the likely behaviour for a flock is to congregate there, settle and feed. However, once PSO has found an optimum, while some particles should explore in the immediate vicinity for any better optimum present, the rest of the swarm should set out to explore new areas. The common PSO practice of only evaluating each particle’s performance at discrete intervals can, at small computational cost, be used to automatically adjust the PSO behaviour in situations where the swarm is ‘settling?so as to encourage part of the swarm to explore further.


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Tim Hendtlass, Tom Rodgers 2008, Discrete Evaluation and the Particle Swarm Algorithm, Complexity International, Volume 12, Paper ID: msid17, URL: http://www.complexity.org.au/vol12/msid17/
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