基于MATLAB的自适应蚁群聚类算法研究与仿真

摘要

介绍了蚁群算法和聚类分析，针对传统蚁群聚类算法参数设置对结果影响大的缺点，提出了一种自适应蚁群聚类算法。主要通过对传统蚁群算法的参数进行简化，增加对局部相似度的运算，使蚂蚁的移动具有一定的方向性，从而降低蚂蚁移动的随机性，对待聚类模式的主成分分析再进行聚类，简化聚类的过程。最后在 MATLAB 上做了仿真实验，实验结果分析表明，运用该算法能得到较好的聚类结果，从而说明该算法是可行的。%　　Introduce the ant colony algorithm and cluster analysis, because of major shortcomings of parameters setting which af-fects the result about traditional ant colony clustering algorithm, proposed an adaptive ant colony clustering algorithm. the original ant colony clustering algorithm has been mainly improved by these methods: Simplify parameters of the traditional ant colony algorithm, in-creasing the computation of local similarity, make the ants move with a certain direction, thereby reduce the randomness of the ants move, and analysis of the principal component analysis of the clustering pattern and then cluster, simplify the clustering process. finally, in the MatLaB simulation and experimental results show that the use of the algorithm can get better clustering results, which demonstrate that the algorithm is feasible.