基于信息熵的蚁群聚类改进方法研究

从模仿蚂蚁堆积尸体的基本模型出发,可以实现蚁群聚类算法.研究了实现基本蚁群算法以及基于信息熵的蚁群聚类算法的关键方法,测试并验证了算法的有效性.同时,在比较其性能的基础上,主要针对基于信息熵的蚁群聚类算法收敛速度快,但却容易陷入局部最优的缺陷,从聚类拆分、合并、孤立点处理等几个方面对基于信息熵的蚁群聚类算法进行了改进,从而在利用其收敛速度快的基础上提高聚类的准确性.仿真实验表明:改进后的方法在聚类的准确性和收敛速度方面都得到了很好的结果,对基于信息熵的蚁群居类算法优化提供了比较好的应用改进.

An improved Ant Colony Clustering Algorithm Using Information Entropy

Ant colony clustering algorithm can be realized on the basis of the basic model that imitates the aggregation of dead bodies by ants.The paper validates LF and entropy based ant colony clustering algorithms via proposing the key simulation methods.Comparing the performance of the two algorithms,it is shown that entropy based ant colony clustering algorithm has the defect of easily getting local optimality although it has better convergence compared with LF.The paper intends to propose an improved entropy ba...