Q-learning whale optimization algorithm for test suite generation with constraints support
This paper introduces a new variant of a metaheuristic algorithm based on the whale optimization algorithm (WOA), the Q-learning algorithm and the Exponential Monte Carlo Acceptance Probability called (QWOA-EMC). Unlike WOA, QWOA-EMC permits just-in-time adaptive selection of its operators (i.e., be...
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| Format: | Article |
| Language: | English English |
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Springer
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/41555/1/Q-learning%20whale%20optimization%20algorithm%20for%20test%20suite%20generation.pdf http://umpir.ump.edu.my/id/eprint/41555/2/Q-learning%20whale%20optimization%20algorithm%20for%20test%20suite%20generation%20with%20constraints%20support.pdf |
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| author | Hassan, Ali Abdullah Salwani, Abdullah Kamal Z., Zamli Rozilawati, Razali |
| author_facet | Hassan, Ali Abdullah Salwani, Abdullah Kamal Z., Zamli Rozilawati, Razali |
| author_sort | Hassan, Ali Abdullah |
| collection | UMP |
| description | This paper introduces a new variant of a metaheuristic algorithm based on the whale optimization algorithm (WOA), the Q-learning algorithm and the Exponential Monte Carlo Acceptance Probability called (QWOA-EMC). Unlike WOA, QWOA-EMC permits just-in-time adaptive selection of its operators (i.e., between shrinking mechanism, spiral shape mechanism, and random generation) based on their historical performances as well as exploits the Monte Carlo Acceptance probability to further strengthen its exploration capabilities by allowing a poor performing operator to be reselected with probability in the early part of the iteration. Experimental results for constraints combinatorial test generation demonstrate that the proposed QWOA-EMC outperforms WOA and performs competitively against other metaheuristic algorithms. |
| first_indexed | 2024-09-25T03:50:20Z |
| format | Article |
| id | UMPir41555 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-09-25T03:50:20Z |
| publishDate | 2023 |
| publisher | Springer |
| record_format | dspace |
| spelling | UMPir415552024-06-11T07:45:50Z http://umpir.ump.edu.my/id/eprint/41555/ Q-learning whale optimization algorithm for test suite generation with constraints support Hassan, Ali Abdullah Salwani, Abdullah Kamal Z., Zamli Rozilawati, Razali QA75 Electronic computers. Computer science This paper introduces a new variant of a metaheuristic algorithm based on the whale optimization algorithm (WOA), the Q-learning algorithm and the Exponential Monte Carlo Acceptance Probability called (QWOA-EMC). Unlike WOA, QWOA-EMC permits just-in-time adaptive selection of its operators (i.e., between shrinking mechanism, spiral shape mechanism, and random generation) based on their historical performances as well as exploits the Monte Carlo Acceptance probability to further strengthen its exploration capabilities by allowing a poor performing operator to be reselected with probability in the early part of the iteration. Experimental results for constraints combinatorial test generation demonstrate that the proposed QWOA-EMC outperforms WOA and performs competitively against other metaheuristic algorithms. Springer 2023 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/41555/1/Q-learning%20whale%20optimization%20algorithm%20for%20test%20suite%20generation.pdf pdf en http://umpir.ump.edu.my/id/eprint/41555/2/Q-learning%20whale%20optimization%20algorithm%20for%20test%20suite%20generation%20with%20constraints%20support.pdf Hassan, Ali Abdullah and Salwani, Abdullah and Kamal Z., Zamli and Rozilawati, Razali (2023) Q-learning whale optimization algorithm for test suite generation with constraints support. Neural Computing and Applications, 35 (34). pp. 24069-24090. ISSN 0941-0643. (Published) https://doi.org/10.1007/s00521-023-09000-2 10.1007/s00521-023-09000-2 |
| spellingShingle | QA75 Electronic computers. Computer science Hassan, Ali Abdullah Salwani, Abdullah Kamal Z., Zamli Rozilawati, Razali Q-learning whale optimization algorithm for test suite generation with constraints support |
| title | Q-learning whale optimization algorithm for test suite generation with constraints support |
| title_full | Q-learning whale optimization algorithm for test suite generation with constraints support |
| title_fullStr | Q-learning whale optimization algorithm for test suite generation with constraints support |
| title_full_unstemmed | Q-learning whale optimization algorithm for test suite generation with constraints support |
| title_short | Q-learning whale optimization algorithm for test suite generation with constraints support |
| title_sort | q learning whale optimization algorithm for test suite generation with constraints support |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/41555/1/Q-learning%20whale%20optimization%20algorithm%20for%20test%20suite%20generation.pdf http://umpir.ump.edu.my/id/eprint/41555/2/Q-learning%20whale%20optimization%20algorithm%20for%20test%20suite%20generation%20with%20constraints%20support.pdf |
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