An enhanced Walrus Optimizer with opposition-based learning and mutation strategy for data clustering
Data clustering plays a crucial role in various domains, such as image processing, pattern recognition, and data mining. Traditional clustering techniques often suffer from limitations like sensitivity to initialization, poor convergence, and entrapment in local optima. To address these challenges,...
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Elsevier
2025-07-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590005625000360 |
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| author | Laith Abualigah Saleh Ali Alomari Mohammad H. Almomani Raed Abu Zitar Hazem Migdady Kashif Saleem Aseel Smerat Vaclav Snasel Absalom E. Ezugwu |
| author_facet | Laith Abualigah Saleh Ali Alomari Mohammad H. Almomani Raed Abu Zitar Hazem Migdady Kashif Saleem Aseel Smerat Vaclav Snasel Absalom E. Ezugwu |
| author_sort | Laith Abualigah |
| collection | DOAJ |
| description | Data clustering plays a crucial role in various domains, such as image processing, pattern recognition, and data mining. Traditional clustering techniques often suffer from limitations like sensitivity to initialization, poor convergence, and entrapment in local optima. To address these challenges, this paper proposes an Enhanced Walrus Optimizer (IWO) tailored for clustering tasks. The proposed IWO integrates two powerful strategies–Opposition-Based Learning (OBL) and Mutation Search Strategy (MSS)–to improve population diversity and prevent premature convergence, thereby enhancing both exploration and exploitation capabilities. These enhancements enable more accurate and stable identification of cluster centers. The effectiveness of IWO is validated through extensive experiments on multiple benchmark clustering datasets and compared against several state-of-the-art metaheuristic algorithms, including PSO, GWO, AOA, and others. The results demonstrate that IWO achieves better results, indicating improved compactness and separation of clusters. Statistical validation using p-values and ranking scores further confirms the superiority of the proposed method. These findings suggest that IWO offers a robust and flexible framework for solving complex clustering problems. Future work will explore hybrid deep learning-integrated models and parallel implementations to enhance scalability. |
| format | Article |
| id | doaj-art-74a219f095964669a4ab53aa272111b6 |
| institution | OA Journals |
| issn | 2590-0056 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Array |
| spelling | doaj-art-74a219f095964669a4ab53aa272111b62025-08-20T02:07:31ZengElsevierArray2590-00562025-07-012610040910.1016/j.array.2025.100409An enhanced Walrus Optimizer with opposition-based learning and mutation strategy for data clusteringLaith Abualigah0Saleh Ali Alomari1Mohammad H. Almomani2Raed Abu Zitar3Hazem Migdady4Kashif Saleem5Aseel Smerat6Vaclav Snasel7Absalom E. Ezugwu8Computer Science Department, Al al-Bayt University, Mafraq 25113, Jordan; Corresponding authors.Faculty of Science and Information Technology, Jadara University, Irbid 21110, JordanDepartment of Mathematics, Facility of Science, The Hashemite University, P.O box 330127, Zarqa 13133, JordanFaculty of Engineering and Computing, Liwa College, Abu Dhabi, United Arab EmiratesCSMIS Department, Oman College of Management and Technology, 320 Barka, OmanDepartment of Computer Science & Engineering, College of Applied Studies & Community Service, King Saud University, Riyadh, 11362, Saudi ArabiaFaculty of Educational Sciences, Al-Ahliyya Amman University, Amman 19328, Jordan; Computer Technologies Engineering, Mazaya University College, Nasiriyah, IraqFaculty of Electrical Engineering and Computer Science, VŠB-Technical University of Ostrava, 70800, Poruba-Ostrava, Czech RepublicUnit for Data Science and Computing, North-West University, 11 Hofman Street, Potchefstroom 2520, South Africa; Corresponding authors.Data clustering plays a crucial role in various domains, such as image processing, pattern recognition, and data mining. Traditional clustering techniques often suffer from limitations like sensitivity to initialization, poor convergence, and entrapment in local optima. To address these challenges, this paper proposes an Enhanced Walrus Optimizer (IWO) tailored for clustering tasks. The proposed IWO integrates two powerful strategies–Opposition-Based Learning (OBL) and Mutation Search Strategy (MSS)–to improve population diversity and prevent premature convergence, thereby enhancing both exploration and exploitation capabilities. These enhancements enable more accurate and stable identification of cluster centers. The effectiveness of IWO is validated through extensive experiments on multiple benchmark clustering datasets and compared against several state-of-the-art metaheuristic algorithms, including PSO, GWO, AOA, and others. The results demonstrate that IWO achieves better results, indicating improved compactness and separation of clusters. Statistical validation using p-values and ranking scores further confirms the superiority of the proposed method. These findings suggest that IWO offers a robust and flexible framework for solving complex clustering problems. Future work will explore hybrid deep learning-integrated models and parallel implementations to enhance scalability.http://www.sciencedirect.com/science/article/pii/S2590005625000360Optimization-based clusteringMetaheuristic algorithmsImproved Walrus OptimizerBenchmark datasetsSwarm intelligence |
| spellingShingle | Laith Abualigah Saleh Ali Alomari Mohammad H. Almomani Raed Abu Zitar Hazem Migdady Kashif Saleem Aseel Smerat Vaclav Snasel Absalom E. Ezugwu An enhanced Walrus Optimizer with opposition-based learning and mutation strategy for data clustering Array Optimization-based clustering Metaheuristic algorithms Improved Walrus Optimizer Benchmark datasets Swarm intelligence |
| title | An enhanced Walrus Optimizer with opposition-based learning and mutation strategy for data clustering |
| title_full | An enhanced Walrus Optimizer with opposition-based learning and mutation strategy for data clustering |
| title_fullStr | An enhanced Walrus Optimizer with opposition-based learning and mutation strategy for data clustering |
| title_full_unstemmed | An enhanced Walrus Optimizer with opposition-based learning and mutation strategy for data clustering |
| title_short | An enhanced Walrus Optimizer with opposition-based learning and mutation strategy for data clustering |
| title_sort | enhanced walrus optimizer with opposition based learning and mutation strategy for data clustering |
| topic | Optimization-based clustering Metaheuristic algorithms Improved Walrus Optimizer Benchmark datasets Swarm intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S2590005625000360 |
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