Particle swarm optimization with mutation operator

Aiming at the shortcoming of basic PSO algorithm, that is, easily plunging into the local minimum, we propose an advanced PSO algorithm with mutation operator. By adding the mutation operator to the algorithm, the advanced algorithm can not only escape from the local minimum's basin of attraction of the later phase, but also maintain the characteristic of fast speed in the early convergence phase. By the contrast experiments of three multimodal test functions and an example whose problem space is non-convex set, it has been proved that the advanced PSO algorithm can improve the global convergence ability, greatly enhance the rate of convergence and overcome the shortcoming of basic PSO algorithm.