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

被引：0

作者：

Yang, Yang ^{[1
,2
]}

Gao, Yuchao ^{[1
,2
]}

Ding, Zhe ^{[3
]}

Wu, Jinran ^{[4
]}

Zhang, Shaotong ^{[5
]}

Han, Feifei ^{[4
]}

Qiu, Xuelan ^{[4
]}

Gao, Shangce ^{[6
]}

Wang, You-Gan ^{[7
]}

机构：

[1] Nanjing Univ Posts & Telecommun, Coll Automat, Nanjing, Peoples R China

[2] Nanjing Univ Posts & Telecommun, Coll Artificial Intelligence, Nanjing, Peoples R China

[3] Queensland Univ Technol, Sch Comp Sci, Brisbane, Qld, Australia

[4] Australian Catholic Univ, Fac Educ & Arts, Banyo, Qld, Australia

[5] Ocean Univ China, Coll Marine Geosci, Frontiers Sci Ctr Deep Ocean Multispheres & Earth, Key Lab Submarine Geosci & Prospecting Tech,MOE, Qingdao, Peoples R China

[6] Univ Toyama, Fac Engn, Toyama, Japan

[7] Univ Queensland, Sch Math & Phys, St Lucia, Qld, Australia

来源：

WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY | 2024年

关键词：

meta-heuristic; optimization; Q-learning; reinforcement learning; PARTICLE SWARM OPTIMIZATION; AUTOMATIC-GENERATION CONTROL; DIFFERENTIAL EVOLUTION; REINFORCEMENT; GRASP;

D O I：

10.1002/widm.1548

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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

引用

页数：37

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