一种混沌差分进化和粒子群优化混合算法*

为了改善差分进化粒子群算法的局部搜索能力和收敛速度，提出了一种混沌差分进化的粒子群优化算法。该算法利用信息交换机制将两组种群分别用差分进化算法和粒子群算法进行协同进化，并且将混沌变异操作引入其中，加强算法的局部搜索能力。通过对三个标准函数进行测试，仿真结果表明该算法与DEPSO算法相比，全局搜索能力、抗早熟收敛性能及收敛速度大大提高。

Hybrid algorithm of chaotic differential evolution and particle swarm optimization

To improve local search ability and convergence speed of differential evolution particle swarm optimization, this paper proposed an algorithm of chaotic differential evolution and the particle swarm optimization. Based on the information exchange mechanism, in the algorithm, used differential evolution algorithm and particle swarm algorithm to make co-evolution for two groups of populations, and introduced the chaos mutation into the algorithm, which enhanced the efficiency of local search capabilities. Using three standard functions to test it, simulation results show that, compared with DEPSO algorithm, this algorithm increase greatly global search ability and resistance to premature convergence.