Metaheuristics from inspiration toward IOS-Intelligent Optimization Systems: A five-generation evolutionary framework

Metaheuristic optimization has evolved from classical handcrafted strategies to increasingly adaptive, hybrid, and intelligence-driven systems. However, existing classification schemes remain largely static and fragmented, limiting their ability to capture the progressive transformation of optimization paradigms. This paper proposes a novel five-generation evolutionary framework (1G–5G) that systematically organizes metaheuristic development from Classical Metaheuristics (1G), Inspiration-Based Metaheuristics (2G), Hybrid Metaheuristics (3G), and Self-Adaptive Metaheuristics (4G), to Intelligent Optimization Systems (IOS, 5G). Unlike conventional taxonomies that group algorithms primarily by inspiration source or structural similarity, the proposed framework introduces an evolutionary perspective based on increasing levels of adaptivity, autonomy, and intelligence integration. The framework further conceptualizes IOS as a system-level optimization paradigm characterized by machine learning integration, autonomous decision-making, surrogate modeling, and dynamic environmental responsiveness. By synthesizing the historical progression of optimization methodologies into a unified generational model, this work provides both a structured analytical taxonomy and a forward-looking conceptual foundation for next-generation intelligent optimization research.