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A New Approach to Improve Particle Swarm Optimization

  • Liping Zhang
  • Huanjun Yu
  • Shangxu Hu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2723)

Abstract

Particle swarm optimization (PSO) is a new evolutionary computation technique. Although PSO algorithm possesses many attractive properties, the methods of selecting inertia weight need to be further investigated. Under this consideration, the inertia weight employing random number uniformly distributed in [0,1] was introduced to improve the performance of PSO algorithm in this work. Three benchmark functions were used to test the new method. The results were presented to show that the new method is effective.

Keywords

Particle Swarm Optimization Particle Swarm Optimization Algorithm Inertia Weight Benchmark Function Global Exploration 
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 2003

Authors and Affiliations

  • Liping Zhang
    • 1
  • Huanjun Yu
    • 1
  • Shangxu Hu
    • 1
  1. 1.College of Material and Chemical EngineeringZhejiang UniversityHangzhouP.R. China

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