关键词: 协同进化, K-均值, 高维优化, 粒子群优化, 局部最优

Abstract: Aimed at particle swarm optimization（PSO） algorithm is easy to fall into local optimal problems for optimizing a high-dimensional population, a new cooperative coevolving particle swarm optimization on K-means cluster（KMS-CCPSO） algorithm is put forward. In the proposed algorithm, the subspace of local search range is designed by K-means algorithm, and the new points’ position and velocity in the search space is relied on Cauchy and Gaussian distributions. The experimental results suggest that the proposed algorithm has better optimization performance, its advantage on the large-scale population optimization problem is more apparent.

Key words: cooperative co-evolution, k-means, high-dimensional optimization, particle swarm optimization, local optimal