An Improved Boosting Bald Eagle Search Algorithm with Improved African Vultures Optimization Algorithm for Data Clustering

Abstract

Data clustering is one of the main issues in the optimization problem. It is the process of clustering a group of items into several groups. Items within each group have the greatest similarity and the least similarity to things in other groups. It is employed in various domains and applications, including biology, business, and consumer analysis, document clustering, web, banking, and image processing, to name a few. In this paper, two new methods are proposed using hybridization of the Bald Eagle Search (BES) Algorithm with the African Vultures Optimization Algorithm (AVOA) (BESAVOA) and BESAVOA with Opposition Based Learning (BESAVOA-OBL) for data clustering. AVOA is used to find the centers of the clusters and improve the centrality of the groups obtained by the BES algorithm. Primary vectors are created based on the population of eagles, and then each vector is used BESAVOA to search the centers of the clusters. The proposed methods (BESAVOA and BESAVOA-OBL) are evaluated on 16 UCI datasets, based on the number of generations, number of iterations, execution time, and convergence. The results show that the BESAVOA-OBL fits better than the other algorithms. The results show that compared to other algorithms, BESAVOA-OBL is more effective by a ratio of 12.42 percent.