Particle Swarm Optimization with Adaptive Mutation

Abstract

A new adaptive mutation particle swarm optimizer, which is based on the variance of the population's fitness, is presented in this paper. During the running time, the mutation probability for the current best particle is determined by two factors: the variance of the population's fitness and the current optimal solution. The ability of particle swarm optimization (PSO) algorithm to break away from the local optimum is greatly improved by the mutation. The experimental results show that the new algorithm not only has great advantage of convergence property over genetic algorithm and PSO, but can also avoid the premature convergence problem effectively.

Keywords

particle swarm, adaptive mutation, optimization, premature convergence