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

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.