Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey-Reference-Cited by-同舟云学术

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

Published:2024-08-18 Issue: Volume: Page:
ISSN:1942-4787
Container-title:WIREs Data Mining and Knowledge Discovery
language:en
Short-container-title:WIREs Data Min & Knowl

Author:

Yang Yang¹,Gao Yuchao¹,Ding Zhe²,Wu Jinran³^ORCID,Zhang Shaotong⁴,Han Feifei³,Qiu Xuelan³,Gao Shangce⁵^ORCID,Wang You‐Gan⁶^ORCID

Affiliation:

1. College of Automation & College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing China

2. School of Computer Science Queensland University of Technology Brisbane Queensland Australia

3. Faculty of Education and Arts Australian Catholic University Banyo Queensland Australia

4. Frontiers Science Center for Deep Ocean Multispheres and Earth System, Key Lab of Submarine Geosciences and Prospecting Techniques, MOE, College of Marine Geosciences Ocean University of China Qingdao China

5. Faculty of Engineering University of Toyama Toyama‐shi Japan

6. School of Mathematics and Physics The University of Queensland St. Lucia Queensland Australia

Abstract

AbstractThis 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

Funder

National Natural Science Foundation of China

Publisher

Wiley

Link

https://wires.onlinelibrary.wiley.com/doi/pdf/10.1002/widm.1548

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