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Hybridisation of Particle Swarm Optimization and Fast Evolutionary Programming

  • Jingsong He
  • Zhengyu Yang
  • Xin Yao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)

Abstract

Particle swarm optimization (PSO) and fast evolutionary programming (FEP) are two widely used population-based optimisation algorithms. The ideas behind these two algorithms are quite different. While PSO is very efficient in local converging to an optimum due to its use of directional information, FEP is better at global exploration and finding a near optimum globally. This paper proposes a novel hybridisation of PSO and FEP, i.e., fast PSO (FPSO), where the strength of PSO and FEP is combined. In particular, the ideas behind Gaussian and Cauchy mutations are incorporated into PSO. The new FPSO has been tested on a number of benchmark functions. The preliminary results have shown that FPSO outperformed both PSO and FEP significantly.

Keywords

Particle Swarm Optimization Benchmark Function Multimodal Function Standard Particle Swarm Optimization Global Good Position 
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 2006

Authors and Affiliations

  • Jingsong He
    • 1
    • 2
  • Zhengyu Yang
    • 1
    • 3
  • Xin Yao
    • 1
    • 4
  1. 1.Nature Inspired Computation and Applications Laboratory 
  2. 2.Department of Electronic Science and Technology 
  3. 3.Department of Computer Science and TechnologyUniversity of Science and Technology of China 
  4. 4.School of Computer ScienceUniversity of Birmingham 

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