Particle Swarm Optimization Algorithm with Adaptive Mutation

Considering the premature convergence problem of Particle Swarm Optimization(PSO),a new Adaptive Particle Swarm Optimization with Mutation(APSOwM) is presented based on the variance ratio of population’s fitness.During the running time,the inertia weight and the mutation probability are determined by two factors: the variance ratio of population’s fitness and the average distance of current population.The ability of APSOwM to break away from the local optimum and to find the global optimum is greatly improved by the adaptive mutation.Experimental results show that the new algorithm is with great advantage of convergence property over PSO,and also avoids the premature convergence problem effectively.