关键词: 聚类分析, 差分演化算法, 模拟退火算法, 自适应技术, K-均值聚类算法

Abstract: The classical k-means clustering runs the risk of being trapped by local optima and its initial classical centers are difficulty in being set.In this paper，a novel k-means cluster analysis algorithm based on self-adaptive hybrid differential evolution with simulated annealing algorithm is proposed to overcome the disadvantage of the classical k-means algorithm.In the proposed algorithm，the choice of learning strategy and several critical control parameters are not required to be pre-specified.During evolution，the suitable learning strategy and parameters setting are gradually self-adapted according to the learning experience.With the aid of simulated annealing strategy，the proposed algorithm is able to improve the global search ability of conventional differential evolution algorithm.Numerical experiment results show that the new algorithms could overcome the faults of the classical k-means algorithm，and converge quickly.Comparative study exposes the two proposed algorithms as competitive algorithms for clustering.

Key words: cluster analysis, differential evolution algorithm, simulated annealing algorithm, self-adaptation, k-means cluster algorithm