In view of the limitation of the traditional particle swarm optimization algorithm in sharing information and the defect of being easy to trap the optimized parameter into local optimum,a PSO algorithm which can share historical optimal information was proposed.In the searching process of the algorithm proposed,the group particles of new generation will share the particle historical optimal information of population in current run,the current global optimal information,and the historical individual optimal information of population in previous run.Five classic functions were used to test the new algorithm's effect,and its stronger global searching ability and faster convergence speed were proved.