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A biogeography-based optimization algorithm for data clustering | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 9، دوره 17، شماره 7، مهر 2026، صفحه 103-112 اصل مقاله (500.09 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2024.33516.4995 | ||
| نویسندگان | ||
| Mohammadreza Shahriari* 1؛ Arash Zaretalab2 | ||
| 1Faculty of Industrial Management, South Tehran Branch, Islamic Azad University, Tehran, Iran | ||
| 2Department of Business Management, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran | ||
| تاریخ دریافت: 21 اسفند 1402، تاریخ بازنگری: 01 اردیبهشت 1403، تاریخ پذیرش: 07 اردیبهشت 1403 | ||
| چکیده | ||
| Data clustering is a pivotal technique in data mining, essential for organizing data into meaningful groups across diverse domains such as engineering, medicine, and biology. This study introduces a Biogeography-based Optimization (BBO) algorithm to optimize data partitioning by effectively navigating the solution space towards optimal cluster configurations. The algorithm leverages migration and mutation mechanisms inspired by natural biogeography to enhance clustering accuracy. The proposed method is evaluated using various datasets of different scales and complexities, and its performance is benchmarked against conventional clustering algorithms, including K-means, Genetic Algorithm (GA), Simulated Annealing (SA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). Comprehensive comparative analyses demonstrate that BBO not only achieves superior clustering accuracy but also exhibits robustness in handling diverse data distributions, underscoring its potential as a valuable tool in data clustering applications. | ||
| کلیدواژهها | ||
| Data Clustering؛ Biogeography-based Optimization؛ k-means؛ ACO؛ PSO؛ GA | ||
| مراجع | ||
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