A new hybrid whale optimization algorithm and golden jackal optimization for data clustering

Data clustering is an unsupervised learning method essential in many different sciences. The main goal of clustering is to find the similarity between samples of a data set to form optimal clusters. The K-Means algorithm is one of the most basic models for data clustering. It strongly depends on the initial search points and leads to local solutions. This book chapter proposes a hybrid model based on Whale Optimization Algorithm (WOA) and Golden Jackal Optimization (GJO) algorithm for the data clustering problem. The GJO algorithm is inspired by the hunting behavior of golden jackals and maintains a good balance between exploration and exploitation. Therefore, the GJO algorithm aims to improve WOA solutions and avoid local optimality. In the proposed model, WOA and GJO mechanisms are used to discover the centers of clusters. The evaluation is performed on eight known datasets from the UCI site. According to the findings, the proposed model performs better than traditional clustering methods in terms of accuracy and eliminates mistakes in the vast majority of the datasets.