Adaptive operator selection with reinforcement learning

Durgut, Rafet; Aydin, Mehmet Emin; Atli, Ibrahim

Abstract

Operator selection plays a crucial role in the efficiency of heuristic-based problem solving algorithms, especially, when a pool of operators is used to let algorithms dynamically select operators to produce new candidate solutions. A sequence of selected operators forms up throughout the search which impacts the success of the algorithms. Successive operators in a bespoke sequence can be complementary and therefore diversify the search while randomly selected operators are not expected to behave in this way. State of art adaptive selection schemes have been proposed to select the best next operator without considering the problem state in the process. In this study, a reinforcement learning algorithm is proposed to embed in a standard artificial bee colony algorithm for taking the problem state on board in operator selection process. The proposed approach implies mapping the problem states to the best fitting operators in the pool so as to achieve higher diversity and shape up an optimum operator sequence throughout the search process. The experimental study successfully demonstrates that the proposed idea works towards higher efficiency. The state of art approaches are outperformed with respect to the quality of solution in solving Set Union Knapsack problem over 30 benchmarking instances.

Citation

Durgut, R., Aydin, M. E., & Atli, I. (2021). Adaptive operator selection with reinforcement learning. Information Sciences, 581, 773-790. https://doi.org/10.1016/j.ins.2021.10.025

• Journal Article Type: Article
• Acceptance Date: Oct 5, 2021
• Online Publication Date: Oct 7, 2021
• Publication Date: 2021-12
• Deposit Date: Oct 9, 2021
• Publicly Available Date: Oct 8, 2022
• Journal: Information Sciences
• Print ISSN: 0020-0255
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 581
• Pages: 773-790
• DOI: https://doi.org/10.1016/j.ins.2021.10.025
• Public URL: https://uwe-repository.worktribe.com/output/7918287
• Publisher URL: https://www.elsevier.com/en-gb