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Simple Dynamic Particle Swarms without Velocity

  • Jorge Peña
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5217)

Abstract

The standard particle swarm optimiser uses update rules including both multiplicative randomness and velocity. In this paper, we look into a general particle swarm model that removes these two features, and study it mathematically. We derive the recursions and fixed points for the first four moments of the sampling distribution, and analyse the transient behaviour of the mean and the variance. Then we define actual instances of the algorithm by coupling the general update rule with specific recombination operators, and empirically test their optimisation efficiency.

Keywords

Particle Swarm Optimiser Particle Swarm Sampling Distribution Transient Behaviour Central Moment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jorge Peña
    • 1
  1. 1.Institut de Mathématiques Appliquées (IMA)Université de LausanneSwitzerland

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