早熟收敛
粒子群优化
数学优化
植绒(纹理)
计算机科学
局部最优
趋同(经济学)
群体行为
可扩展性
收敛速度
数学
钥匙(锁)
经济增长
数据库
计算机安全
复合材料
经济
材料科学
标识
DOI:10.1016/j.asoc.2014.10.026
摘要
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.
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