Machine learning enhancing metaheuristics: a systematic review

• Published in: Soft computing (Berlin, Germany) Vol. 27; no. 21; pp. 15971 - 15998
• Main Authors: da Costa Oliveira, Artur Leandro, Britto, André, Gusmão, Renê
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2023; Springer Nature B.V
• Subjects: Artificial Intelligence; Computational Intelligence; Control; Criteria; Engineering; Evolutionary algorithms; Genetic algorithms; Heuristic methods; Hybridization; Job shop scheduling; Knowledge; Literature reviews; Machine learning; Mathematical Logic and Foundations; Mechatronics; Optimization; Optimization algorithms; Robotics; Search process; Systematic review; Systematic review; Metaheuristic; Optimization; Machine learning
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-023-08886-3

During the optimization process, a large number of data are generated through the search. Machine learning techniques and algorithms can be used to handle the generated data to contribute to the optimization process. The use of machine learning enhancing metaheuristics applied to optimization problems has been drawing attention due to their capacity to add domain knowledge during the search process. This knowledge can accelerate metaheuristics and lead to better and promising solutions. This work provides a systematic literature review of machine learning enhancing metaheuristics and summarizes the current state of the classification of the research field, main techniques and machine learning models, validations strategies, and real-world optimization problems that the approach was applied. Our keyword search found 1.960 papers, published in the last 10 years. After considering the inclusion and exclusion criteria and performing backward snowballing procedure, we have analyzed 111 primary studies. The results show the predominance of the use of surrogate-assisted evolutionary algorithms (SAEAs) for improving the efficiency of the optimization, and the use of estimation of distribution algorithms (EDAs) to increase the effectiveness of the optimization. The objective function value is the mostly applied evaluating criteria to validate the algorithm with other methods. The developed techniques of the studies found are applied in diverse real-world applications such as developing machine learning models, physics simulations with expensive function evaluation, and the variants of the classical job shop scheduling problem. We also discuss trends and opportunities of the research field.