Unite and conquer approach for data clustering based on particle swarm optimization and moth flame optimization

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Mosavi Esra; Shahzadeh Fazeli Seyed Abolfazl; Abbasi Elham; Kaveh Yazdy Fatemeh

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Journal Paper

Paper Information

Journal: IRANIAN JOURNAL OF NUMERICAL ANALYSIS AND OPTIMIZATION Year:2025 | Volume:15 | Issue:2 Page(s): 475-507

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Information Journal Paper

Title

Unite and conquer approach for data clustering based on particle swarm optimization and moth flame optimization

Author(s)

Mosavi Esra | Shahzadeh Fazeli Seyed Abolfazl | Abbasi Elham | Kaveh Yazdy Fatemeh | Issue Writer Certificate

Keywords

Data clustering

k-means clustering

Metaheuristic optimization

Particle swarm optimization

Moth flame optimization

Abstract

Data clustering is a widely used technique in various domains to group data objects according to their similarity. Clustering molecules is a useful process where you can easily subdivide and manipulate and large datasets to group compounds into smaller clusters with similar properties. To dis-cover new molecules with optimal properties and desired biological activity, can be used by comparing molecules and their similarities. A prominent clustering technique is the k-means algorithm, which assigns data objects to the nearest cluster center. However, this algorithm relies on the ini-tial selection of the cluster centers, which can affect its convergence and quality. To address this issue, metaheuristic algorithms have been proposed as a type of approximate optimization algorithm capable of identifying almost optimal solutions. In this paper, a new meta-heuristic approach is proposed by combining two algorithms of Particle swarm optimization (PSO) and Moth flame optimization (MFO), following that, it is used to improve Data clustering. The fficiency of the proposed approach is evaluated utilizing benchmark functions F1-F23. Its efficiency is evaluated with PSO and MFO algorithms on different datasets. Our experiential results show that the suggested approach exceeds the PSO and MFO algorithms with respect to speed of convergence and clustering quality.

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