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Quantum-Behaved Particle Swarm Optimization Algorithm with Controlled Diversity

  • Jun Sun
  • Wenbo Xu
  • Wei Fang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3993)

Abstract

Premature convergence, the major problem that confronts evolutionary algorithms, is also encountered with the Particle Swarm Optimization (PSO) algorithm. In the previous work [11], [12], [13], the Quantum-behaved Particle Swarm (QPSO) is proposed. This novel algorithm is a global-convergence-guaranteed and has a better search ability than the original PSO. But like other evolutionary optimization technique, premature in the QPSO is also inevitable. In this paper, we propose a method of controlling the diversity to enable particles to escape the sub-optima more easily. Before describing the new method, we first introduce the origin and development of the PSO and QPSO. The Diversity-Controlled QPSO, along with the PSO and QPSO is tested on several benchmark functions for performance comparison. The experiment results testify that the DCQPSO outperforms the PSO and QPSO.

Keywords

Particle Swarm Optimization Particle Swarm Evolutionary Computation Particle Swarm Optimization Algorithm Premature Convergence 
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 Intelligent and High Performance Computing, School of Information TechnologySouthern Yangtze UniversityWuxiChina

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