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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Sun, J., Xu, W., & Fang, W. (2006). Quantum-behaved particle swarm optimization algorithm with controlled diversity. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3993 LNCS-III, pp. 847–854). Springer Verlag. https://doi.org/10.1007/11758532_110
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