Gaussian cat swarm optimisation algorithm based on Monte Carlo method for data clustering
Yugal Kumar, Gadadhar Sahoo · International Journal of Computational Science and Engineering · 2017
Clustering is a popular data analysis technique to find the hidden relationship between patterns. It is an unsupervised technique which is applied to obtain the optimal cluster centres especially in partitioned-based clustering algorithms. Cat swarm optimisation (CSO) is a new meta-heuristic algorithm which has been applied to solve various optimisation problems and it provides better results in comparison to same class of algorithms. But, this algorithm suffers from diversity and local optima problems. This study presents a new hybrid CSO algorithm by incorporating Monte Carlo-based search equation and population centroid (PopC) operator with Gaussian probability distribution. Monte Carlo-based search equation is applied to enhance the diversity of the CSO algorithm. The population centroid (PopC) operator is used to prevent the CSO algorithm from trapping in local optima. The performance of the proposed algorithm is tested with several artificial and real datasets and compared with existing methods like K-means and PSO. The experimental results indicate that the proposed algorithm is a robust and effective algorithm to handle the clustering problems.