摘要
微粒群算法因其实现简单及优化效果较好而得到广泛应用,但也存在易早熟和局部收敛的缺点;结合Lévy飞行的特性,提出了一种新的带Lévy变异的微粒群算法,并对其收敛性进行分析,指出该算法依概率收敛于全局最优解.通过对8个标准测试函数的仿真实验,结果表明改进算法中的Lévy变异能够利用粒子的当前知识并增加群体的多样性,从而能够更有效地平衡局部搜索和全局搜索,使其具有更好的性能,最后对改进算法的各参数设置进行了探讨分析.
Particle swarm optimization（PSO） was applied in many fields because of its simplicity and fast convergence,but it is easily prone to be premature and get struck in local optima. Combination with the characteristics of Lévy flight, this paper proposes a new variation of PSO with Lévy mutation（Lévy PSO）, and then analyzed it＇s convergence and pointed out that the algorithm convergence in probability for the global optima. The experiments is conducted on 8 classic benchmark functions, the results show that the Lévy mutation can use the current knowledge of particles and increase the diversity of population. Thus, the proposed algorithm has better performance because of it can more effectively balance the global search and local search. The parameters settings of the proposed algorithm are discussed in the final.
出处
《计算机系统应用》
2016年第10期225-232,共8页
Computer Systems & Applications
基金
国家自然科学基金(61300104)
福建省自然科学基金(2013J01230)
福建软科学项目(2013R0057)
关键词
Lévy分布
微粒群算法
变异
多样性
Lévy distribution
particle swarm optimization
mutation
diversity
