Metaheuristics and Machine Learning Convergence: A Comprehensive Survey and Future Prospects

The integration of machine learning techniques with optimization algorithms has garnered increasing interest in recent years. Two primary purposes emerge from the literature: leveraging metaheuristics in machine learning applications such as regression, classification, and clustering, and enhancing metaheuristics using machine learning to improve convergence time, solution quality, and flexibility. Machine learning techniques offer real-time decision-making capabilities, dimension reduction, and dynamic programming, contributing to robust decision-making processes capable of handling substantial data volumes and addressing stochastic events. To our knowledge, this paper represents the first comprehensive review that explicitly classifies and analyzes both fields, emphasizing their commonalities and delineating the area of their intersection. Through this exploration, our objective is to enrich the understanding of both metaheuristics and machine learning, foster interdisciplinary collaborations, and catalyze innovative approaches that harness the synergies between these two domains. Keywords: Metaheuristics, Machine Learning, Combinatorial Optimization