Particle swarm optimization combined with chaotic and Gaussian mutation

6Chaotic and Gaussian Mutation Particle Swarm Optimization (CGPSO) was proposed to solve the premature and low optimizing precision in standard PSO. In the earlier iterative phase, chaotic mutation was introduced to avoid optimization being trapped into local optimum to enrich the global exploration behavior. In the later iterative phase, Gaussian mutation was incorporated into PSO to fine the solution to improve the local exploitation quality. Simulations show that CGPSO can avoid premature effectively and have the advantages of powerful optimizing ability, more stability, higher optimizing precision and suiting complex function optimization.