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High-Dimension Optimization Problems Using Specified Particle Swarm Optimization

  • Penchen Chou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7331)

Abstract

Particle Swarm Optimization (PSO), proposed by Dr. J. Kennedy and Professor R. Eberhart in 1995, attracts many attentions to solve for a lot of real uni-modal/multi-modal optimization problems. Due to its simplicity of parametersetting and computational efficiency, PSO becomes one of the most popular algorithms for optimization search. Since 1995, many researchers provide different algorithms to set parameters for convergence, explosion and exploitation potential of PSO. Most of the proposed methods are to find a general PSO (called Standard PSO, SPSO) for most of the benchmark problems. However, those may not be suitable to a specified problem, for example, Shaffer or Rosenbrock problems; especially the dimension of the problem is high. On the contrary, with to the difficult problem such as, Rosenbrock, a more proper specified PSO is needed for this high-dimension problem. Therefore, for each problem after more understanding the characteristic of the problem, a SPecified PSO (SPPSO) is proposed. Apply this idea to 5 benchmark problems, such as sphere, quatric, Rosenbrock, Griewank, and Rastrigin functions, four different SPPSO algorithms are proposed with good results in the end.

Keywords

Genetic algorithms particle swarm optimization mutation optimization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Penchen Chou
    • 1
  1. 1.Department of Electrical EngineeringDaYeh UniversityChanghwaTaiwan

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