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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Quantum-Behaved Particle Swarm Optimization Algorithm with Controlled Diversity
verfasst von : Jun Sun, Wenbo Xu, Wei Fang
Erschienen in: Computational Science – ICCS 2006
Verlag: Springer Berlin Heidelberg
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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.