Efficient cluster center optimization: A novel hybrid metaheuristic
Metaheuristics have proved highly effective in addressing optimization challenges. Various algorithms address the clustering problem to find optimal centers for the clusters. One of the disadvantages of some of these algorithms is stagnation in local optima, especially for big data. If this problem...
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| Format: | Article |
| Language: | English |
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Qom University of Technology
2025-03-01
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| Series: | Mathematics and Computational Sciences |
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| Online Access: | https://mcs.qut.ac.ir/article_720760_9b6b311c863d21b8ada0e3b05ea3506c.pdf |
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| author | Saeideh Barkhordari Firozabadi Seyed Abolfazl Shahzadeh Fazeli Jamal Zarepour Ahmadabadi Seyed Mehdi Karbassi |
| author_facet | Saeideh Barkhordari Firozabadi Seyed Abolfazl Shahzadeh Fazeli Jamal Zarepour Ahmadabadi Seyed Mehdi Karbassi |
| author_sort | Saeideh Barkhordari Firozabadi |
| collection | DOAJ |
| description | Metaheuristics have proved highly effective in addressing optimization challenges. Various algorithms address the clustering problem to find optimal centers for the clusters. One of the disadvantages of some of these algorithms is stagnation in local optima, especially for big data. If this problem is not properly solved, the clustering process will suffer. This research introduces a new hybrid method by merging the capabilities of two metaheuristic algorithms: Harris hawks optimization algorithm (HHO) and slime mould algorithm (SMA). These metaheuristic methods are employed to determine the best location for the cluster centers. Optimization aims to reduce intra-cluster distance. In other words, the data points of each cluster should be close to its cluster center and also to avoid local optima. The effectiveness of these techniques is assessed and contrasted with the SMA and HHO algorithms on Iris, Vowel and Wine data sets. Compared to mentioned algorithms, our proposed method exhibits significantly improved convergence speed. The results also proved this method can properly find the optimal centers for clustering which finally improves the performance of the proposed method. |
| format | Article |
| id | doaj-art-053a7c2fcf4b446188e2dbc81d6d919d |
| institution | Kabale University |
| issn | 2717-2708 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Qom University of Technology |
| record_format | Article |
| series | Mathematics and Computational Sciences |
| spelling | doaj-art-053a7c2fcf4b446188e2dbc81d6d919d2025-08-20T03:42:25ZengQom University of TechnologyMathematics and Computational Sciences2717-27082025-03-016111614610.30511/mcs.2025.2041731.1235720760Efficient cluster center optimization: A novel hybrid metaheuristicSaeideh Barkhordari Firozabadi0Seyed Abolfazl Shahzadeh Fazeli1Jamal Zarepour Ahmadabadi2Seyed Mehdi Karbassi3Deparetment of Computer Science, Yazd University, Yazd, IranDeparetment of Computer Science, Yazd University, Yazd, IranDepartment of Computer Science, Yazd University, Yazd, IranDepartment of Mathematical Science, Yazd University, Yazd, IranMetaheuristics have proved highly effective in addressing optimization challenges. Various algorithms address the clustering problem to find optimal centers for the clusters. One of the disadvantages of some of these algorithms is stagnation in local optima, especially for big data. If this problem is not properly solved, the clustering process will suffer. This research introduces a new hybrid method by merging the capabilities of two metaheuristic algorithms: Harris hawks optimization algorithm (HHO) and slime mould algorithm (SMA). These metaheuristic methods are employed to determine the best location for the cluster centers. Optimization aims to reduce intra-cluster distance. In other words, the data points of each cluster should be close to its cluster center and also to avoid local optima. The effectiveness of these techniques is assessed and contrasted with the SMA and HHO algorithms on Iris, Vowel and Wine data sets. Compared to mentioned algorithms, our proposed method exhibits significantly improved convergence speed. The results also proved this method can properly find the optimal centers for clustering which finally improves the performance of the proposed method.https://mcs.qut.ac.ir/article_720760_9b6b311c863d21b8ada0e3b05ea3506c.pdfclusteringmetaheuristicslime mould algorithmharris hawks optimization algorithm |
| spellingShingle | Saeideh Barkhordari Firozabadi Seyed Abolfazl Shahzadeh Fazeli Jamal Zarepour Ahmadabadi Seyed Mehdi Karbassi Efficient cluster center optimization: A novel hybrid metaheuristic Mathematics and Computational Sciences clustering metaheuristic slime mould algorithm harris hawks optimization algorithm |
| title | Efficient cluster center optimization: A novel hybrid metaheuristic |
| title_full | Efficient cluster center optimization: A novel hybrid metaheuristic |
| title_fullStr | Efficient cluster center optimization: A novel hybrid metaheuristic |
| title_full_unstemmed | Efficient cluster center optimization: A novel hybrid metaheuristic |
| title_short | Efficient cluster center optimization: A novel hybrid metaheuristic |
| title_sort | efficient cluster center optimization a novel hybrid metaheuristic |
| topic | clustering metaheuristic slime mould algorithm harris hawks optimization algorithm |
| url | https://mcs.qut.ac.ir/article_720760_9b6b311c863d21b8ada0e3b05ea3506c.pdf |
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