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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Springer
2023
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| Subjects: | |
| 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 http://umpir.ump.edu.my/id/eprint/41555/ https://doi.org/10.1007/s00521-023-09000-2 |
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| Summary: | 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. |
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