Improved Black Hole optimization algorithm for data clustering
Algorithms inspired by nature became more popular in the last few years. They showed up powerful capability in solving optimization problems. This capability was obtained by their ability to be applied individually or by merging them with other algorithms or techniques. The Black Hole optimization a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-09-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157820306212 |
| _version_ | 1798037821048487936 |
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| author | Hasan Deeb Archana Sarangi Debahuti Mishra Shubhendu Kumar Sarangi |
| author_facet | Hasan Deeb Archana Sarangi Debahuti Mishra Shubhendu Kumar Sarangi |
| author_sort | Hasan Deeb |
| collection | DOAJ |
| description | Algorithms inspired by nature became more popular in the last few years. They showed up powerful capability in solving optimization problems. This capability was obtained by their ability to be applied individually or by merging them with other algorithms or techniques. The Black Hole optimization algorithm is a nature-inspired algorithm that belongs to the meta-heuristic category. The Black-Hole algorithm (BH) simulates the black hole phenomenon which is formed from a star with massive size and very high gravitational power. The algorithm starts with a population of a specific size of possible solutions and then gets evaluated by selecting the best one as a black hole. In the suggested modifications, we have introduced a new idea for generating the stars absorbed by the black hole. The star movement towards the black hole has also been modified to increase exploration capabilities. The modified algorithm was used to prove its effectiveness in data clustering without any prior knowledge about the nature of the provided data. Several benchmark datasets and statistical techniques have been used to evaluate the performance of suggested modification. The experiment results promised that the improved algorithm can overcome popular optimization algorithms. |
| first_indexed | 2024-04-11T21:31:47Z |
| format | Article |
| id | doaj.art-01a483ecd6de42ef8d4bf5e402419974 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-04-11T21:31:47Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-01a483ecd6de42ef8d4bf5e4024199742022-12-22T04:01:55ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782022-09-0134850205029Improved Black Hole optimization algorithm for data clusteringHasan Deeb0Archana Sarangi1Debahuti Mishra2Shubhendu Kumar Sarangi3Department of Computer Science Engineering, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, Odisha, IndiaDepartment of Computer Science Engineering, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, Odisha, India; Corresponding author.Department of Computer Science Engineering, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, Odisha, IndiaDepartment of Electronics & Instrumentation Engineering, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, Odisha, IndiaAlgorithms inspired by nature became more popular in the last few years. They showed up powerful capability in solving optimization problems. This capability was obtained by their ability to be applied individually or by merging them with other algorithms or techniques. The Black Hole optimization algorithm is a nature-inspired algorithm that belongs to the meta-heuristic category. The Black-Hole algorithm (BH) simulates the black hole phenomenon which is formed from a star with massive size and very high gravitational power. The algorithm starts with a population of a specific size of possible solutions and then gets evaluated by selecting the best one as a black hole. In the suggested modifications, we have introduced a new idea for generating the stars absorbed by the black hole. The star movement towards the black hole has also been modified to increase exploration capabilities. The modified algorithm was used to prove its effectiveness in data clustering without any prior knowledge about the nature of the provided data. Several benchmark datasets and statistical techniques have been used to evaluate the performance of suggested modification. The experiment results promised that the improved algorithm can overcome popular optimization algorithms.http://www.sciencedirect.com/science/article/pii/S1319157820306212Meta-heuristicBlack holeClusteringOptimization |
| spellingShingle | Hasan Deeb Archana Sarangi Debahuti Mishra Shubhendu Kumar Sarangi Improved Black Hole optimization algorithm for data clustering Journal of King Saud University: Computer and Information Sciences Meta-heuristic Black hole Clustering Optimization |
| title | Improved Black Hole optimization algorithm for data clustering |
| title_full | Improved Black Hole optimization algorithm for data clustering |
| title_fullStr | Improved Black Hole optimization algorithm for data clustering |
| title_full_unstemmed | Improved Black Hole optimization algorithm for data clustering |
| title_short | Improved Black Hole optimization algorithm for data clustering |
| title_sort | improved black hole optimization algorithm for data clustering |
| topic | Meta-heuristic Black hole Clustering Optimization |
| url | http://www.sciencedirect.com/science/article/pii/S1319157820306212 |
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