文章基本信息

标题：Ant Colony Optimization Algorithm Based on Dynamical Pheromones for Clustering Analysis
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作者：Xiaoyong Liu
期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2014
卷号：7
期号：2
页码：29-38
DOI：10.14257/ijhit.2014.7.2.04
出版社：SERSC
摘要：This paper presents an improved clustering algorithm with Ant Colony optimization (ACO) based on dynamical pheromones. Pheromone is an important factor for the performance of ACO algorithms. Two strategies based on adaptive pheromones which improved performance are introduced in this paper. One is to adjust the rate of pheromone evaporation dynamically, named as . , and the other is to adjust the strength of pheromone dynamically, named as Q . Two evaluation indices, Precision and Recall, are chosen to validity the improvement strategies. Numerical simulations demonstrate that the two strategies on pheromone can achieve better performance than basic ant colony algorithm and clustering algorithm with ant colony based on best solution kept.
关键词：Ant Colony Algorithm; Clustering with ACO; Data Mining; Pheromone