关键词: 人工蜂群算法, 聚类分析, K-means, 反向学习, 非线性选择

Abstract: Since the K-means clustering method is sensitive to initial clustering centers and easy to be trapped by local optimum, an Artificial Bee Colony (ABC) clustering algorithm based on K-means was proposed in this paper. This algorithm integrated the improved ABC algorithm with the K-means iteration, which reduced the dependence on the initial clustering centers and the probability to be trapped by local optimum, thus improving the stability of the algorithm. The initialization strategy based on the opposition-based learning improved the diversity of the initial population. The algorithm overcame the problem of premature convergence and improved the efficiency of searching through introducing nonlinear selection strategy. The convergence speed was accelerated and the capability of local optimization was enhanced by dynamically adjusting the neighborhood search range. The experimental results show that the clustering efficiency and performance has been significantly improved, as well as its stability.

Key words: Artificial Bee Colony (ABC) algorithm, clustering analysis, K-means, opposition-based learning, nonlinear selection