An enhanced whale optimization algorithm for clustering

Clustering is a technique of grouping the data objects into clusters. Many metaheuristic algorithms based on swarm intelligence, physic laws, and chemical reactions, among others, have been developed for clustering. In this study, an enhanced whale optimization algorithm (EWOA) is introduced to solve clustering problems. The whale optimization algorithm (WOA) is adapted and enhanced with two additional operational procedures. The position update equations from the water wave optimization algorithm are incorporated into the algorithm to improve the search space and accelerate the convergence rate. The tabu and neighbourhood search mechanisms were added to handle the local optima situation. The efficiency of the proposed EWOA is measured using a simulation-based experiment conducted on eight benchmark datasets, and the results obtained are then compared to seven existing clustering algorithms/techniques. The performance of each algorithm is compared and analyzed using the average intra-cluster distance and f-measure parameters. The experimental results demonstrated the applicability and feasibility of the enhancements that were made and proved the superiority of the proposed EWOA clustering algorithm.