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Enhanced leader PSO (ELPSO): A new PSO variant for solving global optimisation problems
Affiliation:1. School of Informatics Science and Technology, Zhejiang Sci-Tech University, Hangzhou, 310018, China;2. College of Physics and Information Engineering, Minnan Normal University, Zhangzhou 363000, China;3. School of Computer, Wuhan University, Wuhan 430072, China
Abstract: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.
Keywords:Particle swarm optimisation  Global optimisation  Heuristics
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