A comprehensive review of metaheuristic algorithms for ‎combinatorial optimization problems

  • Authors

    • Nazik Saber Rashid AUAS
    • Ibrahim M. I. Zebari Akre University for Applied Sciences, Technical College of Informatics-Akre

    Received date: January 10, 2025

    Accepted date: February 25, 2025

    Published date: February 25, 2025

    https://doi.org/10.14419/d5pxkg39
  • Metaheuristic Algorithms; Combinatorial Optimization; Evolutionary Algorithms; Swarm Intelligence; Hybrid Optimization Frameworks‎.

  • Abstract

    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.

     

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  • How to Cite

    Saber Rashid, N. ., & M. I. Zebari , I. . (2025). A comprehensive review of metaheuristic algorithms for ‎combinatorial optimization problems. International Journal of Scientific World, 11(1), 83-92. https://doi.org/10.14419/d5pxkg39

    Received date: January 10, 2025

    Accepted date: February 25, 2025

    Published date: February 25, 2025