Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art

作     者：Karimi-Mamaghan, Maryam Mohammadi, Mehrdad Meyer, Patrick Karimi-Mamaghan, Amir Mohammad Talbi, El-Ghazali 

作者机构：CNRS UMR 6285 IMT Atlant Lab STICC F-29238 Brest France Univ Tehran Dept Elect & Comp Engn Tehran Iran Univ Lille CNRS UMR 9189Dept Comp Sci Ctr Rech Informat Signal & Automat Lille CRIStAL F-59000 Lille France 

出 版 物：《EUROPEAN JOURNAL OF OPERATIONAL RESEARCH》 (Eur J Oper Res)

年 卷 期：2022年第296卷第2期

页      面：393-422页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 070104[理学-应用数学] 0701[理学-数学] 

主　　题：Meta-heuristics Machine learning Combinatorial optimization problems State-of-the-art 

摘      要：In recent years, there has been a growing research interest in integrating machine learning techniques into meta-heuristics for solving combinatorial optimization problems. This integration aims to lead meta heuristics toward an efficient, effective, and robust search and improve their performance in terms of solution quality, convergence rate, and robustness. Since various integration methods with different purposes have been developed, there is a need to review the recent advances in using machine learning techniques to improve meta-heuristics. To the best of our knowledge, the literature is deprived of having a comprehensive yet technical review. To fill this gap, this paper provides such a review on the use of machine learning techniques in the design of different elements of meta-heuristics for different purposes including algorithm selection , fitness evaluation , initialization , evolution , parameter setting , and cooperation . First, we describe the key concepts and preliminaries of each of these ways of integration. Then, the recent advances in each way of integration are reviewed and classified based on a proposed unified taxonomy. Finally, we provide a technical discussion on the advantages, limitations, requirements, and challenges of implementing each of these integration ways, followed by promising future research directions. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://***/licenses/by-nc-nd/4.0/ )