一种改进的动态改变惯性权重的粒子群算法

Improved PSO Algorithm with Dynamic Change of Inertia Weights

惯性权重在粒子群算法中起到重要的作用,综合考虑了影响惯性权重的几种因素,提出基于进化速度、聚集度和相似度的动态改变惯性权重的粒子群算法,实验证明改进算法在收敛率、收敛精度和全局寻优能力方面都优于几种有代表性的动态改变惯性权重的算法.

Inertia weight is an important parameter in particle swarm optimization algorithm.In this paper,evolution speed,aggregation degree and similarity is integrated into particle swarm optimization organically to control the inertia weight better,and enhance the escaping ability from local optimum when used on complicated problems.The modified PSO algorithm improves the abilities of seeking the global excellent result and convergence accuracy.The experiment results demonstrate that the proposed algorithm are superior to several typical particle swarm optimization algorithms based on dynamic change of inertia weights.