A comprehensive review of metaheuristic algorithms for ‎combinatorial optimization problems - Saber Rashid, Nazik; M. I. Zebari , Ibrahim (International Journal of Scientific World, 2025)
Metaheuristic algorithms are essential for handling difficult combinatorial optimization issues that arise in a variety of domains such as engi-‎neering, logistics, and operations research. These algorithms, based on natural, social, and physical events, strike a compromise between ‎computing efficiency and solution quality. This study divides metaheuristic approaches into three categories: evolutionary algorithms, ‎swarm intelligence techniques, and physics-based models, with a focus on current advances like hybrid and AI-driven frameworks. It also ‎examines issues like as standardization, scalability, and practical implementation, including examples such as the Fire Hawk Optimizer to ‎demonstrate its uses. This study intends to lead the development of trustworthy and efficient metaheuristic algorithms to solve increasingly ‎complicated optimization issues in real-world settings, integrating theoretical ideas and practical examples‎.