Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey

作者：Yang Yang, Yuchao Gao, Zhe Ding, Jinran Wu, Shaotong Zhang, Feifei Han, Xue‐Lan Qiu, Shangce Gao, You‐Gan Wang · 发表于：Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · 年份：2024 · DOI：10.1002/widm.1548 · 被引用次数：34 · 研究领域：Metaheuristic Optimization Algorithms Research、Scheduling and Timetabling Solutions、Robotic Path Planning Algorithms

Abstract 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