A modified particle swarm optimization with differential evolution mutation

Particle swarm optimization (PSO) is a new evolutionary computation technique. The advantage of the PSO over many of the other optimization algorithms is its relative simplicity and quick convergence. But those particles collapse so quickly that it exits a potentially dangerous property: stagnation, which state would make it impossible to arrive at the global optimum, even a local optimum. Under this consideration, a modified particle swarm optimization (MPSO) with differential evolution operator mutations is introduced to eliminate stagnation and avoid premature in this paper. The Probability of trapping at the local optimum during the searching process can be reduced using MPSO. The testing of two multimodal optimization problems shows that MPSO is effective.