Particle Swarm Optimization with Chaotic Mutation
一种带混沌变异的粒子群优化算法

To overcome the disadvantage of low convergence speed and the premature convergence during the later computation period of particle swarm optimization, a chaotic particle swarm optimization (CPSO) was proposed. Aimed to improve the ability to break away from the local optimum and to find the global optimum, the non-winner particles were mutated by chaotic search and the global best position was mutated using the small extent of disturbance according to the variance ratio of population's fitness. The numerical simulation comparing to the standard PSO was performed using of complex benchmark functions with high dimension. The results show that the proposed algorithm can effectively improve both the global searching ability and much better ability of avoiding prcmaturity.