A Hybrid Black Hole Algorithm with Genetic Algorithm for Solving Data Clustering Problems

Abstract

Clustering is a process of randomly selecting k-cluster centers also grouping the data around those centers. Issues of data clustering have recently received research attention and as such, a nature-based optimization algorithm called Black Hole (BH) has said to be suggested as an arrangement to data clustering issues. The BH as a metaheuristic which is elicited from public duplicates the black hole event in the universe, whereas circling arrangement in the hunt space addresses a solo star. Even though primordial BH has shown enhanced execution taking place standard datasets, it doesn't have investigation capacities yet plays out a fine local search. In this paper, another crossover metaheuristic reliant on the mix of BH algorithm as well as genetic algorithm suggested. Genetic algorithm represents its first part of the algorithm which prospects the search space and provides the initial positions for the stars. Then, the BH algorithm utilizes the search space and finds the best solution until the termination condition is reached. The proposed hybrid approach was estimated on synchronized nine popular standard functions where the outcomes indicated that the process generated enhanced outcome with regard to robustness compared to BH and the benchmarking algorithms in the study. Furthermore, it also revealed a high convergence rate which used six real datasets sourced of the UCI machine learning laboratory, indicating fine conduct of the hybrid algorithm on data clustering problems. Conclusively, the investigation showed the suitability of the suggested hybrid algorithm designed for resolving data clustering issues.