A Multi-objective Modified Particle Swarm Optimization (MMPSO) technique with an application to data clustering

Among the nature inspired heuristic or metaheuristic optimization algorithms Particle Swarm Optimization(PSO) algorithms are widely used to solve clustering problem. In this paper, a modified multi-objective PSO (MMPSO) algorithm is proposed for data clustering. In the proposed MMPSO, the intra-cluster distance and inter-cluster distance are considered as the objective functions. In-order to improve the convergence rate of the MMPSO, a Cloning concept is introduced. To validate the proposed algorithms, five standard data sets are considered. The result shows that, the proposed method gives better quality solution as compared to some existing well known algorithms. The simulation results infer that the proposed algorithms can be efficiently used for data clustering.