Swarm-Based Optimisation Strategies for Structural Engineering: A Case Study on Welded Beam Design

Swarm intelligence (SI) algorithms have emerged as powerful tools for solving complex structural optimisation problems that are characterised by nonlinearity, multiple constraints, and multimodal objective functions. This paper presents a comprehensive comparative study of five prominent swarm-based metaheuristic algorithms—Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), Artificial Bee Colony (ABC), Grey Wolf Optimiser (GWO), and Harris Hawks Optimisation (HHO)—applied to the classical welded beam design problem. The design objective is to minimise fabrication cost while satisfying structural and geometric constraints. Each algorithm is implemented in a unified benchmarking environment, and their performances are evaluated in terms of solution quality, convergence speed, robustness, and computational cost. The results reveal nuanced performance trade-offs among the algorithms, highlighting the importance of balancing exploration and exploitation, as well as parameter sensitivity, in engineering applications. The study contributes to the growing body of research in computational structural engineering, offering insights into the practical application of swarm intelligence methods for real-world design challenges.