Machine Learning for Enhancing Metaheuristics in Global Optimization: A Comprehensive Review

The integration of machine learning (ML) with metaheuristic optimization has emerged as one of the most promising frontiers in artificial intelligence and global search. Metaheuristics offer flexibility and effectiveness in solving complex optimization problems where gradients are unavailable or unreliable, but often struggle with premature convergence, parameter sensitivity, and poor scalability. ML techniques—especially supervised, unsupervised, reinforcement, and meta-learning—provide powerful tools to address these limitations through adaptive, data-driven, and intelligent search strategies. This review presents a comprehensive survey of ML-enhanced metaheuristics for global optimization. We introduce a functional taxonomy that categorizes integration strategies based on their role in the optimization process, from operator control and surrogate modeling to landscape learning and learned optimizers. We critically analyze representative techniques, identify emerging trends, and highlight key challenges and future directions. The paper aims to serve as a structured and accessible resource for advancing the design of intelligent, learning-enabled optimization algorithms.