Abstract: For the difficulty of finding all the extreme solutions for multi-peak function optimization, a particle swarm optimization Memetic algorithm is proposed. It combines the advantages of particle swarm optimization in global search and Memetic algorithm in local search. So, it enhances the searing ability of the algorithm. The experiments results show that the algorithm has better effectiveness and rapid convergence.

Key words: particle swarm optimization, multi-peak function, Memetic algorithm

摘要： 针对目前多峰函数优化问题较难找到全部局部最优解的情况，提出了一种粒子群Memetic算法。算法结合了粒子群优化的全局搜索能力和爬山法的局部搜索能力，增强了算法搜索最优解的能力。实验结果表明，该算法求解精度较高，且收敛速度较快。

关键词: 粒子群优化, 多峰函数, Memetic算法