基于全局信息的人工蜂群聚类算法

Artificial Colony Clustering Algorithm based on Global Information

针对人工蜂群算法易陷入局部最优和收敛速度慢的不足,提出了一种基于全局信息的人工蜂群聚类算法.基于全局信息的人工蜂群聚类算法通过加入食物源平均丰富度(richness),利用中间聚类效果,更好地更新食物源;并且通过引入全局最优信息,提高跟随蜂的搜索效率,以获取聚类问题的全局最优解.同时在UCI机器学习库的4个标准数据集上进行了大量的实验来评估算法的性能.并将该算法和基本人工蜂群算法、粒子群算法和Kmeans算法进行比较.实验结果证明提出的基于全局信息的人工蜂群聚类算法具有更好的性能.

An improved artificial bee colony algorithm was proposed for data clustering aiming at overcoming the shortcomings of being trapped in local optimum and slow convergence rate in this paper, called artificial colonyclustering algorithm based on global information （GI-ABC）. On the one hand, the modified algorithm added the average richness of food to the updating equation of food source, taking advantage of the intermediate clustering consequence to update the food source better . On the other hand , it utilized global information to improve the search efficiency of the onlooker bees, which contributed to getting the global optimum. Meanwhile, abundant experiments were conducted to evaluate it. In the experiments, four of topical real data sets selected from the UCI Machine Learning Repository were used to test the performance of the strategies compared with the other clustering algorithms, such as ABC algorithm, Particle Swarm Optimization （PSO） algorithm and K-means algorithm. The results indicate that the modified algorithm can generate better results than other algorithms and has better general performance.