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

•A comprehensive review on the integration of machine learning into meta-heuristics.•A unified taxonomy to provide a common terminology and classification.•Classification of numerous articles based on different characteristics.•Technical discussions on advantages, limitations, requirements, and chal...

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Published in:European journal of operational research Vol. 296; no. 2; pp. 393 - 422
Main Authors: Karimi-Mamaghan, Maryam, Mohammadi, Mehrdad, Meyer, Patrick, Karimi-Mamaghan, Amir Mohammad, Talbi, El-Ghazali
Format: Journal Article
Language:English
Published: Elsevier B.V 01.01.2022
Elsevier
Subjects:
Artificial Intelligence
Combinatorial optimization problems
Computer Science
Machine learning
Meta-heuristics
Operations Research
State-of-the-art
Combinatorial optimization problems
Machine learning
State-of-the-art
Meta-heuristics
ISSN:0377-2217, 1872-6860
Online Access:Get full text
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Abstract •A comprehensive review on the integration of machine learning into meta-heuristics.•A unified taxonomy to provide a common terminology and classification.•Classification of numerous articles based on different characteristics.•Technical discussions on advantages, limitations, requirements, and challenges.•Proposition of promising future research directions. 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.
AbstractList •A comprehensive review on the integration of machine learning into meta-heuristics.•A unified taxonomy to provide a common terminology and classification.•Classification of numerous articles based on different characteristics.•Technical discussions on advantages, limitations, requirements, and challenges.•Proposition of promising future research directions. 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.
Author Karimi-Mamaghan, Amir Mohammad
Mohammadi, Mehrdad
Karimi-Mamaghan, Maryam
Meyer, Patrick
Talbi, El-Ghazali
Author_xml – sequence: 1
  givenname: Maryam
  orcidid: 0000-0003-2889-5769
  surname: Karimi-Mamaghan
  fullname: Karimi-Mamaghan, Maryam
  email: maryam.karimi@imt-atlantique.fr
  organization: IMT Atlantique, Lab-STICC, UMR CNRS 6285, Brest F-29238, France
– sequence: 2
  givenname: Mehrdad
  surname: Mohammadi
  fullname: Mohammadi, Mehrdad
  email: mehrdad.mohammadi@imt-atlantique.fr
  organization: IMT Atlantique, Lab-STICC, UMR CNRS 6285, Brest F-29238, France
– sequence: 3
  givenname: Patrick
  orcidid: 0000-0001-6479-213X
  surname: Meyer
  fullname: Meyer, Patrick
  email: patrick.meyer@imt-atlantique.fr
  organization: IMT Atlantique, Lab-STICC, UMR CNRS 6285, Brest F-29238, France
– sequence: 4
  givenname: Amir Mohammad
  surname: Karimi-Mamaghan
  fullname: Karimi-Mamaghan, Amir Mohammad
  email: amir.karimi@ut.ac.ir
  organization: Department of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
– sequence: 5
  givenname: El-Ghazali
  surname: Talbi
  fullname: Talbi, El-Ghazali
  email: el-ghazali.talbi@univ-lille.fr
  organization: Department of Computer Science, University of Lille, CNRS UMR 9189, Centre de Recherche en Informatique Signal et Automatique de Lille (CRIStAL), Lille F-59000, France
BackLink https://imt-atlantique.hal.science/hal-03231097$$DView record in HAL
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Keywords Combinatorial optimization problems
Machine learning
State-of-the-art
Meta-heuristics
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Snippet •A comprehensive review on the integration of machine learning into meta-heuristics.•A unified taxonomy to provide a common terminology and...
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SubjectTerms Artificial Intelligence
Combinatorial optimization problems
Computer Science
Machine learning
Meta-heuristics
Operations Research
State-of-the-art
Title Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art
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