Quantum-Behaved particle swarm optimization algorithm with controlled diversity

  • Authors:

  • Jun Sun;Wenbo Xu;Wei Fang

  • Affiliations:

  • Center of Intelligent and High Performance Computing, School of Information Technology, Southern Yangtze University, Wuxi, Jiangsu, China;Center of Intelligent and High Performance Computing, School of Information Technology, Southern Yangtze University, Wuxi, Jiangsu, China;Center of Intelligent and High Performance Computing, School of Information Technology, Southern Yangtze University, Wuxi, Jiangsu, China

  • Venue:
  • ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part III
  • Year:
  • 2006

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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.