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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:European journal of operational research Ročník 296; číslo 2; s. 393 - 422
Hlavní autori: Karimi-Mamaghan, Maryam, Mohammadi, Mehrdad, Meyer, Patrick, Karimi-Mamaghan, Amir Mohammad, Talbi, El-Ghazali
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.01.2022
Elsevier
Predmet:
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
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:•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.
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2021.04.032