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Evaluation of Comprehensive Learning Particle Swarm Optimizer

  • Jing J. Liang
  • A. Kai Qin
  • Ponnuthurai Nagaratnam Suganthan
  • S. Baskar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3316)

Abstract

Particle Swarm Optimizer (PSO) is one of the evolutionary computation techniques based on swarm intelligence. Comprehensive Learning Particle Swarm Optimizer (CLPSO) is a variant of the original Particle Swarm Optimizer which uses a new learning strategy to make the particles have different learning exemplars for different dimensions. This paper investigates the effects of learning proportion P c in the CLPSO, showing that different P c realizes different performance on different problems.

Keywords

Particle Swarm Particle Swarm Optimizer Algorithm Swarm Intelligence Premature Convergence Benchmark Function 
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 2004

Authors and Affiliations

  • Jing J. Liang
    • 1
  • A. Kai Qin
    • 1
  • Ponnuthurai Nagaratnam Suganthan
    • 1
  • S. Baskar
    • 1
  1. 1.BLK S-2, School of Electrical and Electronic EngineeringNanyang Technological UniversitySingapore

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