Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems

Abstract

This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in order to address the problem of data clustering. The process that groups similar items within a dataset into non-overlapping groups. Grey wolf hunting behaviour served as the model for grey wolf optimizer, however, it frequently lacks the exploration and exploitation capabilities that are essential for efficient data clustering. This work mainly focuses on enhancing the grey wolf optimizer using a new weight factor and the K-means algorithm concepts in order to increase variety and avoid premature convergence. Using a partitional clustering-inspired fitness function, the K-means clustering-based grey wolf optimizer was extensively evaluated on ten numerical functions and multiple real-world datasets with varying levels of complexity and dimensionality. The methodology is based on incorporating the K-means algorithm concept for the purpose of refining initial solutions and adding a weight factor to increase the diversity of solutions during the optimization phase. The results show that the K-means clustering-based grey wolf optimizer performs much better than the standard grey wolf optimizer in discovering optimal clustering solutions, indicating a higher capacity for effective exploration and exploitation of the solution space. The study found that the K-means clustering-based grey wolf optimizer was able to produce high-quality cluster centres in fewer iterations, demonstrating its efficacy and efficiency on various datasets. Finally, the study demonstrates the robustness and dependability of the K-means clustering-based grey wolf optimizer in resolving data clustering issues, which represents a significant advancement over conventional techniques. In addition to addressing the shortcomings of the initial algorithm, the incorporation of K-means and the innovative weight factor into the grey wolf optimizer establishes a new standard for further study in metaheuristic clustering algorithms. The performance of the K-means clustering-based grey wolf optimizer is around 34% better than the original grey wolf optimizer algorithm for both numerical test problems and data clustering problems.

Using Hybrid Mountain Gazelle Optimization and Particle Swarm Optimization Algorithms to Improve Clustering

“…Demirci et al [12] proposed an electrical search algorithm (ESA) based on the movement of electricity in high-resistive areas such as wood, glass, and gases and applied it to the clustering problem. Premkumar et al [26] focus on enhancing the grey wolf optimizer using a new weight factor and the concepts of the k means algorithm to increase variety and avoid premature convergence. In the following, the required algorithms will be outlined.…”

Hybrid rice optimization algorithm inspired grey wolf optimizer for high-dimensional feature selection

“…In 2014, the Grey Wolf Optimizer (GWO) was proposed 25 , a population-based metaheuristic algorithm that mimics the social hierarchy and group hunting behavior of grey wolves. Owing to its inherent simplicity, fewer requirements for control parameters, and strong optimization performance, the GWO has found extensive applications across engineering problems ?, 26 , anomaly detection 27 , band selection 28 , path planning 29,30 , FS [31][32][33] , and other fields [34][35][36] . Wang et al 37 developed a role-oriented binary GWO.…”