A Modified Particle Swarm Optimization Based on Wavelet Mutation

In order to improve the particle swarm optimization algorithm’s demerits such as low convergence precision and avoid relapsing into local optima, a new algorithm is proposed to overcome the demerits. On one hand, the mean best position is introduced to change particle’s velocity update rule to help the particle acquire more information of others to adjust its movement, on the other hand, wavelet mutation mechanism is introduced into the mean best position to improve the swarm diversity. The experimental results show that the proposed algorithm has powerful optimizing ability, good stability and higher optimizing precision.