Hybrid whale optimization algorithm for enhancing K-means clustering technique

This chapter presents Hybrid Whale Optimization Algorithm (HWOA) to tackle the stubborn problems of local optima traps and initialization sensitivity of the K-means clustering technique. This work was inspired by the popularity and robustness of meta-heuristic algorithms in providing compelling solutions, which sparked several effective approaches and computational tools to address challenging real-world problems. The Chameleon Swarm Algorithm (CSA) is embedded with the bubble-net mechanism of WOA to help the search agents of HWOA effectively explore and exploit each potential area of the search space, enhancing the capability of both exploitation and exploration aspects of the classic WOA. Additionally, the search agents of HWOA use a rotation mechanism to relocate to new spots outside of nearby areas to conduct global exploration. This process increases the search efficiency of WOA while also enhancing the diversity and intensity behavior of the search agents. These improvements to HWOA increase its capacity for exploitation and broaden the range of search scopes and directions in performing clustering tasks. To assess the effectiveness of the proposed HWOA on clustering activities, a total of ten distinct datasets from the UCI are used, each with a different level of complexity. According to the experimental findings, the proposed HWOA outperforms eight meta-heuristic algorithms-based clustering and the conventional K-means clustering technique by a statistically significant margin in terms of performance distance metric.