Metaheuristics in Sustainable and Green Optimization

The accelerating global pursuit of sustainability has placed optimization at the forefront of achieving environmental, economic, and social balance. This study presents a comprehensive review of metaheuristic algorithms as powerful computational tools for addressing sustainable and green optimization challenges. By examining a broad range of classical and modern metaheuristics—including bio-inspired, physics-based, swarm intelligence, and hybrid models—this work explores how these algorithms are utilized to minimize energy consumption, carbon emissions, and resource waste across key sectors such as renewable energy systems, smart grids, sustainable manufacturing, and green logistics. The paper emphasizes the role of hybrid and intelligent adaptive metaheuristics in enhancing convergence speed, robustness, and scalability in complex, multi-objective optimization scenarios. Comparative analyses reveal the superiority of hybrid models in achieving accurate, energy-efficient, and environmentally responsible outcomes. Furthermore, the study highlights persistent challenges related to computational cost, parameter sensitivity, and real-time adaptability. By consolidating current findings and identifying open research directions—such as self-adaptive learning-based frameworks, unified benchmarking standards, and quantum-inspired metaheuristics—this review underscores the transformative potential of metaheuristic optimization in advancing the global sustainability agenda.