This paper reviews the integration of Q‐learning with meta‐heuristic algorithms (QLMA) over the last 20 years, highlighting its success in solving complex optimization problems. We focus on key aspects of QLMA, including parameter adaptation, operator selection, and balancing global exploration with local exploitation. QLMA has become a leading solution in industries like energy, power systems, and engineering, addressing a range of mathematical challenges. Looking forward, we suggest further exploration of meta‐heuristic integration, transfer learning strategies, and techniques to reduce state space.This article is categorized under: Technologies > Computational Intelligence Technologies > Artificial Intelligence

中文翻译：

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Q-learning 元启发式优化算法的进展：一项调查


本文回顾了过去 20 年 Q 学习与元启发式算法 (QLMA) 的集成，强调了其在解决复杂优化问题方面的成功。我们专注于 QLMA 的关键方面，包括参数自适应、算子选择以及平衡全局探索与本地开发。 QLMA 已成为能源、电力系统和工程等行业的领先解决方案，解决了一系列数学挑战。展望未来，我们建议进一步探索元启发式集成、迁移学习策略和减少状态空间的技术。本文分类如下：技术 > 计算智能技术 > 人工智能