一种带混沌变异的粒子群优化算法

Particle Swarm Optimization with Chaotic Mutation

为了克服粒子群算法在进化后期存在收敛速度慢、易陷入局部极小等问题,提出了一种混沌变异粒子群优化算法。该算法根据群体适应度变化率对种群中非优胜粒子进行变异操作,并对全局最优位置进行小范围混沌扰动,以增强算法跳出局部最优的能力。对几种复杂典型函数与标准粒子群算法进行了仿真测试,结果表明该算法明显改善了全局搜索能力和抗早熟收敛性能。

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.