Metaheuristic Algorithms for Engineering and Combinatorial‎Optimization: A Comparative Study Across Problems Categories and Benchmarks

  • Authors

    • Awaz Ahmed Shaban Information Technology Department, Technical College of Informatics-Akre, Akre University for Applied Sciences, Duhok, Iraq
    • Saman M. Almufti Department of Computer Science, College of Science, Knowledge University, Erbil, Iraq and Department of Computer Systems, Ararat Technical Institute, Duhok, Iraq
    • Renas Rajab Asaad Department of Computer Science, College of Science, Knowledge University, Erbil, Iraq
    https://doi.org/10.14419/0hndc578

    Received date: June 22, 2025

    Accepted date: August 11, 2025

    Published date: August 23, 2025

  • Optimization Problem Categories; Metaheuristic Algorithms; Engineering Design Optimization; Benchmark Problems; Combinatorial Optimization; Swarm Intelligence; Constraint Handling; Algorithm Comparison; Structural Design; NP-Hard Problems.

  • Abstract

    Optimization remains a cornerstone of modern engineering and computational intelligence, playing a vital role in the design, control, and ‎allocation of limited resources across industries ranging from logistics to structural engineering. Traditional optimization methods, such as ‎gradient-based and exact algorithms, often struggle with the nonlinear, multimodal, and constrained nature of real-world problems, necessitating the adoption of metaheuristic approaches. These biologically and physically inspired algorithms offer flexibility, scalability, and robustness in navigating complex search spaces.‎

    This study presents a systematic categorization of optimization problems—including combinatorial, continuous, constrained, and multi-‎objective classes—followed by a rigorous comparative analysis of nine prominent metaheuristics: Ant Colony Optimization (ACO), Lion ‎Algorithm (LA), Cuckoo Search (CS), Grey Wolf Optimizer (GWO), Vibrating Particles System (VPS), Social Spider Optimization (SSO), ‎Cat Swarm Optimization (CSO), Bat Algorithm (BA), and Artificial Bee Colony (ABC). The algorithms are evaluated across five representative benchmark problems: the Traveling Salesman Problem (TSP), Welded Beam Design (WBD), Pressure Vessel Design (PVD), ‎Tension/Compression Spring Design (TSD), and the Knapsack Problem (KP).‎

    Key contributions include: 1)Domain-specific suitability analysis, revealing how algorithmic mechanisms align with problem structures.‎

    ‎ 2) Performance benchmarking under standardized conditions, highlighting convergence speed, solution quality, and constraint-handling ‎efficacy. 3) Practical insights for practitioners on algorithm selection, hybridization potential, and adaptation challenges.‎

    Results demonstrate that no single algorithm dominates universally; instead, problem characteristics dictate optimal choices. For instance, ‎ACO excels in discrete problems (TSP, KP), while GWO and BA outperform in continuous engineering designs (WBD, PVD). The study ‎concludes with recommendations for future research, including dynamic parameter tuning, hybrid models, and real-world scalability ‎assessments‎.

    Author Biography

    • Saman M. Almufti, Department of Computer Science, College of Science, Knowledge University, Erbil, Iraq and Department of Computer Systems, Ararat Technical Institute, Duhok, Iraq
      Swarm intellignce

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  • How to Cite

    Ahmed Shaban, A., Almufti, S. M., & Asaad , R. R. . (2025). Metaheuristic Algorithms for Engineering and Combinatorial‎Optimization: A Comparative Study Across Problems Categories and Benchmarks. International Journal of Scientific World, 11(2), 38-49. https://doi.org/10.14419/0hndc578