Authors:
Saman M. Almufti, Awaz Ahmed Shaban

Addresses:
Department of Computer Science, College of Science, Knowledge University, Erbil, Iraq. Department of Information Technology, Technical College of Informatics-Akre, Akre University for Applied Sciences, Duhok, Iraq.

Abstract:

Metaheuristic algorithms have emerged as indispensable tools for solving complex structural design problems characterized by nonlinearity, high dimensionality, and conflicting objectives. Traditional optimisation techniques often fall short in navigating such multifaceted design landscapes, necessitating more adaptive and robust approaches. This paper presents a comparative analysis of five metaheuristic algorithms applied to structural engineering problems: Genetic Algorithms (GA), Osprey Optimisation Algorithm (OOA), Quantum Annealing-based Structural Optimisation (QASO), Ensemble Laplacian Biogeography-Based Sine Cosine Algorithm (ELBBSC), and the Fitness Distance Balance Modified Metaheuristic (FDB-Meta). Recent developments since 2020 are emphasized to highlight innovations in convergence dynamics, robustness, and computational efficiency. Benchmark structural problems, such as steel moment frames, cable-stayed bridges, and RC slab bridges, are used to evaluate algorithm performance across various criteria, including convergence speed, solution quality, robustness, scalability, and parameter sensitivity. The results indicate that while GA remains a foundational method, newer algorithms, such as FDB-Meta and OOA, demonstrate significant improvements in both cost efficiency and reliability. This study contributes a systematic guideline for algorithm selection in structural design optimisation and outlines avenues for future research in hybrid metaheuristic development and adaptive parameter tuning.

Keywords: Structural Design Optimization; Metaheuristic Algorithms; Genetic Algorithm; Osprey Optimization Algorithm; Quantum Annealing; FDB-Meta; Convergence Speed; Solution Quality; Parameter Sensitivity.

Received on: 14/08/2024, Revised on: 25/10/2024, Accepted on: 03/12/2024, Published on: 07/03/2025

DOI: 10.69888/FTSIN.2025.000368

FMDB Transactions on Sustainable Intelligent Networks, 2025 Vol. 2 No. 1, Pages: 33-48

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