Synergistic integration of metaheuristics and machine learning: latest advances and emerging trends

Ruining Zhang, Jian Wang, Chanjuan Liu, Kaile Su, Hisao Ishibuchi, Yaochu Jin

Abstract: Metaheuristic algorithms (MH) and machine learning (ML) are important components of artificial intelligence (AI). The synergy between MH’s optimization search capabilities and ML’s data analysis strengths has proven to be highly effective, providing a powerful combination for delivering high-quality solutions across diverse fields. Particularly in real-world applications such as autonomous driving and healthcare, the integration of MH and ML can significantly enhance the intelligence level and decision-making efficiency of systems, addressing the urgent societal and industrial demands for high-efficiency and high-precision solutions. This paper clarifies the logic behind the surge in research on MH-ML hybrid algorithms and addresses the gaps in current reviews regarding their timeliness and breadth of perspective. We begin by elucidating the fundamental concepts underpinning MH and ML, followed by a comprehensive classification framework that categorizes and synthesizes the latest research findings systematically. The paper concludes with an exploration of the challenges inherent in MH-ML hybrid algorithms and proposes future research directions. The analysis of the collected literature demonstrates that the integration of MH and ML generally enhances the performance of algorithms in specific problems. Despite progress from simple combinations to deeper integrations, challenges such as theoretical lag and interpretability remain. Future research will focus on solving these challenges by exploring further integration, using open-source tools, and adapting across diverse domains to expand the use of MH-ML hybrid algorithms.