基于改进蚁群算法的聚类分析

CLUSTERING ANALYSIS BASED ON IMPROVED ANT COLONY ALGORITHM

聚类在数据挖掘、统计学、机器学习等很多领域都有很大应用。聚类问题可以归结为一个优化问题。蚁群算法(Ant Colony Algorithm)已成功地解决了许多组合优化的难题。介绍一种蚁群聚类算法,并进行了优化,提出一种改进的蚁群聚类算法。它改进了蚂蚁搜索解的方法,并引入均匀交叉算子,将蚁群算法和遗传算法融合。它提高进化速度,有效改善了蚁群算法易于过早地收敛于非最优解的缺陷。仿真实验取得了较好的结果。

Clustering has its roots in many areas,including data mining,statistics,and machine learning. Clustering can be regarded as an optimization problem. Ant colony algorithm was applied successfully in solving many hard combinatorial optimization problems. An ant colony clustering algorithm is introduced and optimized in the article. An improved ant colony clustering algorithm is put forward. It improves the method of ant searching its solutions and introduces the idea of crossover to combine the ant colony algorithm with the genetic algorithm. It improves the speed of evolution and effectively ameliorates the disadvantage of ant colony algorithm in easily falling in local best. The simulation experiments on the algorithm achieves quite good outcome.