Enhancing global search ability of quantum-behaved particle swarm optimization by maintaining diversity of the swarm

Authors:
Jun Sun;Wenbo Xu;Wei Fang
Affiliations:
Center of Computational Intelligence and High Performance Computing, School of Information Technology, Southern Yangtze University, Wuxi Jiangsu, China;Center of Computational Intelligence and High Performance Computing, School of Information Technology, Southern Yangtze University, Wuxi Jiangsu, China;Center of Computational Intelligence and High Performance Computing, School of Information Technology, Southern Yangtze University, Wuxi Jiangsu, China
Venue:
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Year:
2006

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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.