Multi-Population Black Hole Algorithm for the Problem of Data Clustering

Salih, Sinan and Alsewari, AbdulRahman and Waheb, H. A. and Mohammed, Mustafa K. A. and Rashid, Tarik A. and Das, Debashish and Basurra, Shadi (2023) Multi-Population Black Hole Algorithm for the Problem of Data Clustering. PLOS ONE. pp. 1-39. ISSN 1932-6203

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Abstract

The retrieval of important information from a dataset requires applying a special data mining technique known as data clustering (DC). DC classifies similar objects into a groups of similar characteristics. Clustering involves grouping the data around k-cluster centres that typically are selected randomly. Recently, the issues behind DC have called for a search for an alternative solution. Recently, a nature-based optimization algorithm named Black Hole Algorithm (BHA) was developed to address the several well-known optimization problems. The BHA is a metaheuristic (population-based) that mimics the event around the natural phenomena of black holes, whereby an individual star represents the potential solutions revolving around the solution space. The original BHA algorithm showed better performance compared to other algorithms when applied to a benchmark dataset, despite its poor exploration capability. Hence, this paper presents a multi-population version of BHA as a generalization of the BHA called MBHA wherein the performance of the algorithm is not dependent on the best-found solution but a set of generated best solutions. The method formulated was subjected to testing using a set of nine widespread and popular benchmark test functions. The ensuing experimental outcomes indicated the highly precise results generated by the method compared to BHA and comparable algorithms in the study, as well as excellent robustness. Furthermore, the proposed MBHA achieved a high rate of convergence on six real datasets (collected from the UCL machine learning lab), making it suitable for DC problems. Lastly, the evaluations conclusively indicated the appropriateness of the proposed algorithm to resolve DC issues.

Item Type:	Article
Identification Number:	10.1371/journal.pone.0288044
Dates:	Date Event 5 June 2023 Accepted 26 June 2023 Published Online
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science CAH11 - computing > CAH11-01 - computing > CAH11-01-03 - information systems CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions:	Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Abdulrahman Alsewari
Date Deposited:	28 Jun 2023 11:09
Last Modified:	06 Oct 2023 15:27
URI:	https://www.open-access.bcu.ac.uk/id/eprint/14517

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