基于交换机制的自适应蚁群聚类算法

A self-adaptive ACO clustering algorithm based on swap mechanism

在研究了基本聚类模型的基础上,模拟蚂蚁寻找食物源的行为,提出了一种基于蚁群最优化的自适应聚类分析的新方法。与之前的蚁群聚类不同,引入交换机制增强蚁群的觅食能力以提高聚类性能。该算法可以不用预先输入聚类数目,在仿真实验中该方法获得了比GCA算法和K-means算法更好的表现,表明这种基于交换机制的聚类算法具有较好的聚类性能。

Based on the basic clustering model, a self-adaptive clustering algorithm based on ant colony optimization was proposed. The algorithm simulated the behavior of ants in search of food sources. Different from the previous ant colony clustering, it introduced the swap mechanisms to enhance ant colony＇s foraging ability to improve the clustering performance. This algorithm can be no pre - entering the number of clusters. In the simulation results, our algorithm obtain better performance than the GCA algorithm and K-means algorithm. It shows that the swap mechanism-based clustering algorithm has better clustering performance.