DIGWO: Hybridization of Dragonfly Algorithm with Improved Grey Wolf Optimization Algorithm for Data Clustering

Abstract

: Data present in great quantity raises the difficulty of managing them that affects the effectual decision-making procedure. Therefore, data clustering achieves notable significance in knowledge extraction and a well-organized clustering algorithm endorses the effectual decision making. For that reason, an algorithm for data clustering by exploiting the DIGWO method is presented in this paper, which decides the optimal centroid to perform the clustering procedure. The developed DIGWO technique exploits the calculation steps of the Dragonfly Algorithm (DA) with the incorporation of the Improved Grey Wolf Optimization (IGWO) with a novel formulated fitness model. Moreover, the proposed method exploits the least fitness measure to position the optimal centroid and the fitness measure based upon three constraints, such as intra-cluster distance, intercluster distance, and cluster density. The optimal centroid ensuing to the minimum value of the fitness is exploited for clustering the data. Simulation is performed by exploiting three datasets and the comparative evaluation is performed that shows that the performance of the developed method is better than the conventional algorithms such as Grey Wolf Optimization (GWO), Dragonfly and Particle Swarm Optimization (PSO).