A Dynamic Evolutionary Algorithm for Multimodal Function Optimization

As a new evolutionary algorithm, particle swarm optimization (PSO) algorithm has been gained much attention and wide applications in different fields during the past decade. However, for nonlinear, no differentiable and multi-modal problems, the PSO algorithm often suffers the problem of being trapped in local optima so as to be premature convergence. To enhance the performance of standard PSO, the particle velocity variation strategy (PVVS) is introduced in PSO, and variation PSO (VPSO) algorithm is proposed. Compared with SPSO, The results of the numerical experiment show that the proposed method can not only significantly speed up the convergence, but also effectively search the global optimal point.