一种基于信息素的蚁群聚类算法

An Ant Colony Clustering Algorithm Based on Pheromones

提出了一种改进的蚁群聚类分析算法,通过改进LF算法中群体相似度函数,加入参数的自适应调整策略,利用短期记忆和网格信息素的局部分布控制蚂蚁的随机移动,并结合蚂蚁速度动态变化、半径递增、强制放下等特性。采用测试数据和不同的算法进行了对比实验分析,仿真实验结果表明,该算法显示出了较高的稳定性和准确率。

An improved ant clustering analysis algorithm is proposed,which modifies the similarity function of the Lumer–Faieta（LF） groups,and adds parameters to adjust the adaptive strategy,controls the random movement of ants by using the short-term memory and grid of the local pheromone distribution, combining with dynamic changing of ants speed,increasing radius,forcing down. Comparison experiments are performed by different algorithms with testing data. The resultsindicate thatthe proposed algorithm showsahigh stability and ahighaccuracy.