ACPSO: Hybridization of Ant Colony and Particle Swarm Algorithm for Optimization in Data Clustering using Multiple Objective Functions

K-means clustering groups the similar information using distance function. Even though it is a good algorithm for grouping, it may affect the clustering performance in terms of cluster initialization. This directed to new research track on emerging better algorithms with good initial centroids. This paper gives a hybrid algorithm, called ACPSO algorithm for optimal clustering process. ACO algorithm is used in this paper for the discovery centroids with the stimulation of ant colony system. Once initial centroids are produced by ACO algorithm, PSO algorithm is applied to find optimal cluster with the help of different fitness function, namely, XB index, Sym index, DB index, Connected DB index, Connected Dunn index and Mean Square Distance. Finally, experimentation is performed with iris data and performance is evaluated with five different evaluation metrics. The experimental results shows the proposed method's performance is good as compared with existing algorithm in most of evaluation metrics.