The new fitness function has also been proposed to guide the search accurately as it plays a vital role for performance evaluation of metaheuristic based clustering techniques. The performance of ACGSA has been evaluated on real-life datasets with respect to three clustering metrics namely the number of clusters, inter-cluster and intra-cluster distance. The results have been compared with state-of-the-art clustering techniques such as Automatic Clusting using Modified Differential Evolution(ACDE), Dynamic Clustering using Particle Swarm Optimization(DCPSO) abd Genetic Clustering with an unknown number of clusters (GCUK) (Bandyophyay and Maulik, 2002)