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Article type: Research Article
Authors: Nasef, Mohammed M.a; * | El Kafrawy, Passent M.a; b | Hashim, Amala; c
Affiliations: [a] Mathematics and Computer Science Department Faculty of Science, Menoufia University, Shebin El-Koom, Egypt | [b] School of Information Technology and Computer Science, Nile University, Egypt | [c] Information Systems Department Higher Institute of Advanced Studies, Haram, Giza, Egypt
Correspondence: [*] Corresponding author. Mohammed M. Nasef, Mathematics and Computer Science Department Faculty of Science, Menoufia University, Shebin El-Koom, 32511, Egypt. Email: [email protected].
Abstract: Computational models are foundational concepts in computer science; many of these models such as P systems are based on natural biological processes. P systems represent a wide framework for a variety of concepts of data mining, as models of data clustering approaches. Data clustering is a technique for analyzing data based on its structure that is widely utilized for many applications. In this paper, the proposed model (PSO-MFM) has combined the Particle Swarm Optimization algorithm (PSO) with Mitochondrial Fusion Model to overcome some constraints of clustering techniques. The solving of clustering problem based on particle swarm is investigated in the proposed model when mutual dynamic rules are used. It can find the best cluster centers for a data set and improve clustering performance by utilizing the distributed parallel computing concept of mutual dynamic rules of mitochondrial fusion model. The comparative results demonstrate that the proposed strategy outperforms competition models when it comes to clustering accuracy, stability and the most efficient in time complexity.
Keywords: Particle swarm optimization, P systems, mitochondrial fusion model, mutual dynamic rules
DOI: 10.3233/JIFS-223804
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3071-3083, 2024
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