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

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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.

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Availability of data and materials

The authors confirm that the data supporting the findings of this study come from different sources. The sources are as follows:

The data used in Sect. 5.2 are available within the article [9] and its supplementary materials.

The data used in Sect. 5.3 are available within the article [4] and its supplementary materials.

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Acknowledgements

This work was supported by the Ministry of Education, Malaysia (FRGS /1/2019/ICT02/UKM/01/1), and the Universiti Kebangsaan Malaysia (DIP-2016-024). Ali Abdullah Hassan would like to express his gratitude to Hadhramout Foundation in Yemen for their tuition fee support.

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Authors and Affiliations

  1. Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia

    Ali Abdullah Hassan, Salwani Abdullah & Rozilawati Razali

  2. Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600, Pekan, Pahang, Malaysia

    Kamal Z. Zamli

  3. Faculty of Science and Technology, Universitas Airlangga, C Campus JI. Dr. H. Soekamo, Mulyorejo, Surabaya, 60115, Indonesia

    Kamal Z. Zamli

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  1. Ali Abdullah Hassan
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  2. Salwani Abdullah
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Correspondence to Ali Abdullah Hassan.

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Hassan, A.A., Abdullah, S., Zamli, K.Z. et al. Q-learning whale optimization algorithm for test suite generation with constraints support. Neural Comput & Applic 35, 24069–24090 (2023). https://doi.org/10.1007/s00521-023-09000-2

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