基于K-均值聚类的协同进化粒子群优化算法

摘要

针对粒子群优化（PSO）算法优化高维问题时，易陷入局部最优，提出一种基于K-均值聚类的协同进化粒子群优化（KMS-CCPSO）算法。该算法通过引入K-均值算法扩大种群的局部搜索范围，采用柯西分布和高斯分布相结合的方法更新粒子的位置。实验结果表明，该算法具有较好的优化性能，其优势在处理高维问题上更为明显。%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 experi-mental results suggest that the proposed algorithm has better optimization performance, its advantage on the large-scale population optimization problem is more apparent.