Reinforcement Learning Based Acceptance Criteria for Metaheuristic Algorithms
Abstract This paper proposes novel reinforcement learning-based acceptance criteria for metaheuristic algorithms. We develop Q-learning and Deep Q-learning-based acceptance criteria and integrate them into simulated annealing (SA) and artificial bee colony (ABC) algorithms. Also, we design two versi...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-08-01
|
| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-025-00924-2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849234490243678208 |
|---|---|
| author | Oğuzhan Ahmet Arık Gülhan Toğa Berrin Atalay |
| author_facet | Oğuzhan Ahmet Arık Gülhan Toğa Berrin Atalay |
| author_sort | Oğuzhan Ahmet Arık |
| collection | DOAJ |
| description | Abstract This paper proposes novel reinforcement learning-based acceptance criteria for metaheuristic algorithms. We develop Q-learning and Deep Q-learning-based acceptance criteria and integrate them into simulated annealing (SA) and artificial bee colony (ABC) algorithms. Also, we design two versions of these novel acceptance criteria: the online version and the offline version of Q-learning and deep Q-learning based acceptance criteria. The online version starts to train itself and to make decisions to accept or reject the candidate solution with the start of the metaheuristic. The offline version uses a trained and well-tuned Q-learning and deep Q-learning based acceptance criteria to make accept/reject decisions. Our experimental study compares our proposed acceptance criteria with existing ones, such as fuzzy rule-based acceptance (FRBA) and simulated annealing-like acceptance (SALA) criteria. The experiment reveals that metaheuristics with deep Q-learning-based offline acceptance criteria outperform metaheuristics with existing acceptance criteria and other variants in this study. |
| format | Article |
| id | doaj-art-d19b11c2f4094be4b10d6c208b4daa2e |
| institution | Kabale University |
| issn | 1875-6883 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj-art-d19b11c2f4094be4b10d6c208b4daa2e2025-08-20T04:03:07ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832025-08-0118112810.1007/s44196-025-00924-2Reinforcement Learning Based Acceptance Criteria for Metaheuristic AlgorithmsOğuzhan Ahmet Arık0Gülhan Toğa1Berrin Atalay2Department of Industrial Engineering, Faculty of Engineering, Erciyes UniversityDepartment of Industrial Engineering, Faculty of Engineering, Erciyes UniversityDepartment of Industrial Engineering, Faculty of Engineering, Erciyes UniversityAbstract This paper proposes novel reinforcement learning-based acceptance criteria for metaheuristic algorithms. We develop Q-learning and Deep Q-learning-based acceptance criteria and integrate them into simulated annealing (SA) and artificial bee colony (ABC) algorithms. Also, we design two versions of these novel acceptance criteria: the online version and the offline version of Q-learning and deep Q-learning based acceptance criteria. The online version starts to train itself and to make decisions to accept or reject the candidate solution with the start of the metaheuristic. The offline version uses a trained and well-tuned Q-learning and deep Q-learning based acceptance criteria to make accept/reject decisions. Our experimental study compares our proposed acceptance criteria with existing ones, such as fuzzy rule-based acceptance (FRBA) and simulated annealing-like acceptance (SALA) criteria. The experiment reveals that metaheuristics with deep Q-learning-based offline acceptance criteria outperform metaheuristics with existing acceptance criteria and other variants in this study.https://doi.org/10.1007/s44196-025-00924-2Reinforcement learningDeep Q learningQ learningFuzzy rule basedSimulated annealingArtificial bee colony |
| spellingShingle | Oğuzhan Ahmet Arık Gülhan Toğa Berrin Atalay Reinforcement Learning Based Acceptance Criteria for Metaheuristic Algorithms International Journal of Computational Intelligence Systems Reinforcement learning Deep Q learning Q learning Fuzzy rule based Simulated annealing Artificial bee colony |
| title | Reinforcement Learning Based Acceptance Criteria for Metaheuristic Algorithms |
| title_full | Reinforcement Learning Based Acceptance Criteria for Metaheuristic Algorithms |
| title_fullStr | Reinforcement Learning Based Acceptance Criteria for Metaheuristic Algorithms |
| title_full_unstemmed | Reinforcement Learning Based Acceptance Criteria for Metaheuristic Algorithms |
| title_short | Reinforcement Learning Based Acceptance Criteria for Metaheuristic Algorithms |
| title_sort | reinforcement learning based acceptance criteria for metaheuristic algorithms |
| topic | Reinforcement learning Deep Q learning Q learning Fuzzy rule based Simulated annealing Artificial bee colony |
| url | https://doi.org/10.1007/s44196-025-00924-2 |
| work_keys_str_mv | AT oguzhanahmetarık reinforcementlearningbasedacceptancecriteriaformetaheuristicalgorithms AT gulhantoga reinforcementlearningbasedacceptancecriteriaformetaheuristicalgorithms AT berrinatalay reinforcementlearningbasedacceptancecriteriaformetaheuristicalgorithms |