Modified Black Hole Algorithm with Genetic Operators
In this paper, a modified version of nature-inspired optimization algorithm called Black Hole has been proposed. The proposed algorithm is population based and consists of genetic algorithm operators in order to improve optimization results. The proposed method enhances Black Hole algorithm performa...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2016-08-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/25868717/view |
| _version_ | 1811300951565271040 |
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| author | Saber Yaghoobi Hamed Mojallali |
| author_facet | Saber Yaghoobi Hamed Mojallali |
| author_sort | Saber Yaghoobi |
| collection | DOAJ |
| description | In this paper, a modified version of nature-inspired optimization algorithm called Black Hole has been proposed. The proposed algorithm is population based and consists of genetic algorithm operators in order to improve optimization results. The proposed method enhances Black Hole algorithm performance by searching space with more diversity. The modified Black Hole algorithm has been applied to a well-known benchmark. The experimental results show that the modified Black Hole algorithm outperforms compared to some prominent optimization algorithms. |
| first_indexed | 2024-04-13T07:00:11Z |
| format | Article |
| id | doaj.art-b85e01011d0a49e8aac2d0678c1ae645 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-04-13T07:00:11Z |
| publishDate | 2016-08-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-b85e01011d0a49e8aac2d0678c1ae6452022-12-22T02:57:08ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832016-08-019410.1080/18756891.2016.1204114Modified Black Hole Algorithm with Genetic OperatorsSaber YaghoobiHamed MojallaliIn this paper, a modified version of nature-inspired optimization algorithm called Black Hole has been proposed. The proposed algorithm is population based and consists of genetic algorithm operators in order to improve optimization results. The proposed method enhances Black Hole algorithm performance by searching space with more diversity. The modified Black Hole algorithm has been applied to a well-known benchmark. The experimental results show that the modified Black Hole algorithm outperforms compared to some prominent optimization algorithms.https://www.atlantis-press.com/article/25868717/viewBlack HoleNature-inspired optimizationMetaheuristic algorithmBenchmarking |
| spellingShingle | Saber Yaghoobi Hamed Mojallali Modified Black Hole Algorithm with Genetic Operators International Journal of Computational Intelligence Systems Black Hole Nature-inspired optimization Metaheuristic algorithm Benchmarking |
| title | Modified Black Hole Algorithm with Genetic Operators |
| title_full | Modified Black Hole Algorithm with Genetic Operators |
| title_fullStr | Modified Black Hole Algorithm with Genetic Operators |
| title_full_unstemmed | Modified Black Hole Algorithm with Genetic Operators |
| title_short | Modified Black Hole Algorithm with Genetic Operators |
| title_sort | modified black hole algorithm with genetic operators |
| topic | Black Hole Nature-inspired optimization Metaheuristic algorithm Benchmarking |
| url | https://www.atlantis-press.com/article/25868717/view |
| work_keys_str_mv | AT saberyaghoobi modifiedblackholealgorithmwithgeneticoperators AT hamedmojallali modifiedblackholealgorithmwithgeneticoperators |