期刊
APPLIED SOFT COMPUTING
卷 26, 期 -, 页码 401-417出版社
ELSEVIER
DOI: 10.1016/j.asoc.2014.10.026
关键词
Particle swarm optimisation; Global optimisation; Heuristics
Particle swarm optimisation (PSO) is a well-established optimisation algorithm inspired from flocking behaviour of birds. The big problem in PSO is that it suffers from premature convergence, that is, in complex optimisation problems, it may easily get trapped in local optima. In this paper, a new PSO variant, named as enhanced leader PSO (ELPSO), is proposed for mitigating premature convergence problem. ELPSO is mainly based on a five-staged successive mutation strategy which is applied to swarm leader at each iteration. The experimental results confirm that in all terms of accuracy, scalability and convergence rate, ELPSO performs well. (C) 2014 Elsevier B.V. All rights reserved.
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