A Self-Adaptive Mutation-Particle Swarm Optimization Algorithm

A self-adaptive mutation-particle swarm optimization algorithm is proposed in this paper. In this algorithm, firstly, to avoid the randomness of updating particle velocity, a modified velocity updating formula of the particle which varies with convergence factor and the diffusion factor is proposed by adaptive inertia weight. Secondly, the introduction of stochastic mutation operators enhances the global search capability of the particles by providing additional diversity. Thirdly, to avoid local optimum a modified global search strategy is employed. Simulations for six benchmark test functions show that IPSO remarkably improves the calculation accuracy and has better ability to find the global optimum than that of the standard PSO algorithm.