Abstract: In order to preserve the varieties of the swarm and avoid to be in premature convergence,a Particle Swarm Optimization（PSO） algorithm based on the differential evolution mutation is proposed．The new algorithm uses the particle aggregation quality to judge that if the particles in the swarm are congregative so much,then apply the differential evolution to mutate the self prevenient best position of each particle,in order to realize the aim of preserving the varieties of the swarm.Then,this new PSO and the standard PSO are used to resolve four well—known and widely used test functions’ optimization problems．Results show that the new PSO has greater efficiency,better performance and more advantages than the standard PSO in many aspects.