Abstract: Inertia weight is an important parameter in particle swarm optimization algorithm. In this paper, evolution speed, aggregation degree and similarity is integrated into particles warm optimization organiically 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 dynammic change of inertia weights.