基于进化停滞周期的局部变异PSO算法及其收敛性分析

为了克服粒子群优化算法容易陷入局部最优而发生早熟收敛的问题,提出一种基于进化停滞周期的局部变异粒于群优化算法.算法引入进化停滞周期和近期全局最优位置的概念,使粒子的飞行受近期全局最优位置影响,并在种群进化停滞时对随机选中的局部粒子执行变异操作,增加种群多样性,扩大搜索范围,提高求解质量.算法用种群进化停滞周期代替多样性度量,避免了多样性计算引起的高计算复杂度.对于几个常用基准函数的仿真结果验证了算法的合理性和有效性.

A PSO algorithm with local mutation in evolution stagnation cycle and its convergence analysis

To overcome the premature of particle swarm optimization(PSO) algorithm, an algorithm called particle swarm
optimization with local mutation in evolution stagnation cycle(LSPSO) is presented. Concepts of evolution stagnation cycle and recent global best position are proposed, so that particles are influenced by recent global best position instead of global best position, and a random local mutation operation of particles is taken when the evolution of population stagnates. These strategies enrich the diversity of population, extend the search space, and improve the quality of solution. Instead of computing the diversity of population, evolution stagnation cycle is used for lower computing complexity. The simulation results show the reasonability and effectiveness of the algorithm.