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An Improved Particle Swarm Pareto Optimizer with Local Search and Clustering

  • Ching-Shih Tsou
  • Hsiao-Hua Fang
  • Hsu-Hwa Chang
  • Chia-Hung Kao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)

Abstract

In this paper, the local search and clustering mechanism are incorporated into the Multi-Objective Particle Swarm Optimization (MOPSO). The local search mechanism prevents premature convergence, hence enhances the convergence of optimizer to true Pareto-optimal front. The clustering mechanism reduces the nondominated solutions to a handful number such that we can speed up the search and maintain the diversity of the nondominated solutions. The performance of this approach is evaluated on metrics from literature. The results against a three objectives optimization problem show that the proposed Pareto optimizer is competitive with the strength Pareto evolutionary algorithm (SPEA) in converging towards the front and generates a well-distributed nondominated set.

Keywords

Particle Swarm Optimization Local Search Pareto Front Pareto Optimizer Nondominated Solution 
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

  • Ching-Shih Tsou
    • 1
  • Hsiao-Hua Fang
    • 2
  • Hsu-Hwa Chang
    • 1
  • Chia-Hung Kao
    • 2
  1. 1.Department of Business AdministrationNational Taipei College of BusinessTaipeiTaiwan
  2. 2.Department of Information ManagementShih Hsin UniversityTaipeiTaiwan

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