One of the central issues that must be resolved for a metaheuristic optimization process to work well is the dilemma of the balance between exploration and exploitation. The metaheuristics (MH) that achieved this balance can be called balanced MH, where a Q-Learning (QL) integration framework was proposed for the selection of metaheuristic operators conducive to this balance, particularly the selection of binarization schemes when a continuous metaheuristic solves binary combinatorial problems. In this work the use of this framework is extended to other recent metaheuristics, demonstrating that the integration of QL in the selection of operators improves the exploration-exploitation balance. Specifically, the Whale Optimization Algorithm and the Sine-Cosine Algorithm are tested by solving the Set Covering Problem, showing statistical improvements in this balance and in the quality of the solutions.

中文翻译：

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Q-Learnheuristics：迈向数据驱动的平衡元启发式

要使元启发式优化过程正常运行，必须解决的核心问题之一是探索和开发之间的平衡困境。达到这种平衡的元启发式（MH）可以称为平衡MH，其中提出了Q-Learning（QL）集成框架来选择有利于这种平衡的元启发式算子，特别是连续元启发式求解二进制时的二值化方案的选择组合问题。在这项工作中，该框架的使用扩展到其他最近的元启发式，证明 QL 在算子选择中的集成改善了探索 - 开发的平衡。具体来说，通过求解集合覆盖问题来测试 Whale 优化算法和 Sine-Cosine 算法，