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Cask Theory Based Parameter Optimization for Particle Swarm Optimization

  • Zenghui Wang
  • Yanxia Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7928)

Abstract

To avoid the bored try and error method of finding a set of parameters of Particle Swarm Optimization (PSO) and achieve good optimization performance, it is desired to get an adaptive optimization method to search a good set of parameters. A nested optimization method is proposed in this paper and it can be used to search the tuned parameters such as inertia weight ω, acceleration coefficients c1 and c2, and so on. This method considers the cask theory to achieve a better optimization performance. Several famous benchmarks were used to validate the proposed method and the simulation results showed the efficiency of the proposed method.

Keywords

PSO Parameter Optimization Try and Error method Nested Optimization method Cask theory 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Zenghui Wang
    • 1
  • Yanxia Sun
    • 2
  1. 1.Department of Electrical and Mining engineeringUniversity of South AfricaFloridaSouth Africa
  2. 2.Department of Electrical engineeringTshwane University of TechnologyPretoriaSouth Africa

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