摘要： 针对普通粒子群优化算法难以在动态环境下有效逼近最优位置的问题，提出一种动态粒子群优化算法。设置敏感粒子和响应阈值，当敏感粒子的适应度值变化超过响应阈值时，按一定比例重新初始化种群和粒子速度。设计双峰DF1动态模型，用于验证该算法的性能，仿真实验结果表明其动态极值跟踪能力较强。

关键词: 粒子群优化算法, 动态, 双峰DF1模型, 敏感粒子

Abstract: Aiming at the problem that normal Particle Swarm Optimization(PSO) algorithm can not approach the best position effectively in dynamic environment, this paper proposes a dynamic PSO algorithm. It sets sensing particle and response threshold. When sensing particle’s fitness change exceeds response threshold, the algorithm reinitializes the swarm and particle velocity. It designs double-hump DF1 dynamic model to validate the capability of this algorithm. Simulation experimental results show that it has high ability of dynamic extremum tracing.

Key words: Particle Swarm Optimization(PSO) algorithm, dynamic, double-hump DF1 model, sensitive particle

中图分类号: 

• TP18