Q-learning whale optimization algorithm for test suite generation with constraints support

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)

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DOI/Official URL: https://doi.org/10.1007/s00521-023-09000-2

Abstract

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.

Item Type:	Article
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Combinatorial testing; Constrained software testing; Meta-heuristic; Q-learning algorithm; Reinforcement learning; Test case generation; Whale optimization algorithm
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division:	Faculty of Computing
Depositing User:	Miss Amelia Binti Hasan
Date Deposited:	11 Jun 2024 06:26
Last Modified:	11 Jun 2024 07:45
URI:	http://umpir.ump.edu.my/id/eprint/41555
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