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Enhancing Global Search Ability of Quantum-Behaved Particle Swarm Optimization by Maintaining Diversity of the Swarm

  • Jun Sun
  • Wenbo Xu
  • Wei Fang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4259)

Abstract

Premature convergence, the major problem that confronts evolu-tionary algorithms, is also encountered with the Particle Swarm Optimization (PSO) algorithm. Quantum-behaved Particle Swarm (QPSO), a novel variant of PSO, is a global-convergence-guaranteed algorithm and has a better search ability than the original PSO. But like PSO and other evolutionary optimization techniques, premature in QPSO is also inevitable. The reason for premature convergence in PSO or QPSO is that the information flow between particles makes the diversity of the population decline rapidly. In this paper, we propose Diversity-Maintained QPSO (DMQPSO). Before describing the new method, we first introduce the origin and development of PSO and QPSO. DMQPSO, along with the PSO and QPSO, is tested on several benchmark functions for performance comparison. The experiment results show that the DMQPSO outperforms the PSO and QPSO in many cases.

Keywords

Particle Swarm Optimization Particle Swarm Premature Convergence Benchmark Function Swarm Size 
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

  • Jun Sun
    • 1
  • Wenbo Xu
    • 1
  • Wei Fang
    • 1
  1. 1.Center of Computational Intelligence and High Performance Computing, School of Information TechnologySouthern Yangtze UniversityWuxi JiangsuChina

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