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

Yang Yang; Yuchao Gao; Zhe Ding; Jinran Wu; Shaotong Zhang; Feifei Han; Xuelan Qiu; Shangce Gao; You Gan Wang

doi:10.1002/widm.1548

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

Yang Yang, Yuchao Gao, Zhe Ding^*, Jinran Wu^*, Shaotong Zhang, Feifei Han, Xuelan Qiu, Shangce Gao, You Gan Wang

^*Corresponding author for this work

Intellectual Information Engineering

Research output: Contribution to journal › Review article › peer-review

8 Scopus citations

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.

Original language	English
Article number	e1548
Journal	Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Volume	14
Issue number	6
DOIs	https://doi.org/10.1002/widm.1548
State	Published - 2024/11/01

Keywords

meta-heuristic
optimization
Q-learning
reinforcement learning

ASJC Scopus subject areas

General Computer Science

Access to Document

10.1002/widm.1548

Cite this

@article{5e83aff2241f461b87372dba89225583,

title = "Advancements in Q-learning meta-heuristic optimization algorithms: A survey",

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.",

keywords = "meta-heuristic, optimization, Q-learning, reinforcement learning",

author = "Yang Yang and Yuchao Gao and Zhe Ding and Jinran Wu and Shaotong Zhang and Feifei Han and Xuelan Qiu and Shangce Gao and Wang, {You Gan}",

note = "Publisher Copyright: {\textcopyright} 2024 Wiley Periodicals LLC.",

year = "2024",

month = nov,

day = "1",

doi = "10.1002/widm.1548",

language = "英語",

volume = "14",

journal = "Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery",

issn = "1942-4787",

publisher = "John Wiley & Sons Inc.",

number = "6",

}

TY - JOUR

T1 - Advancements in Q-learning meta-heuristic optimization algorithms

T2 - A survey

AU - Yang, Yang

AU - Gao, Yuchao

AU - Ding, Zhe

AU - Wu, Jinran

AU - Zhang, Shaotong

AU - Han, Feifei

AU - Qiu, Xuelan

AU - Gao, Shangce

AU - Wang, You Gan

PY - 2024/11/1

Y1 - 2024/11/1

N2 - 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.

AB - 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.

KW - meta-heuristic

KW - optimization

KW - Q-learning

KW - reinforcement learning

UR - http://www.scopus.com/inward/record.url?scp=85201366698&partnerID=8YFLogxK

U2 - 10.1002/widm.1548

DO - 10.1002/widm.1548

M3 - 総説

AN - SCOPUS:85201366698

SN - 1942-4787

VL - 14

JO - Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

JF - Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

IS - 6

M1 - e1548

ER -

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

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this