A comparative study between Gray Wolf and particle swarm algorithms use for optimization of cost in composite beam

The application of Particle Swarm Optimization (PSO) and Gray Wolf Optimization (GWO) as meta-heuristic techniques for cost optimization of composite beams (CBs) was evaluated. The performance of PSO and GWO was compared to the existing techniques in the literature, and the results demonstrated that the cost-saving achieved with PSO and GWO was significantly better. The PSO and GWO were also compared to Social Harmony Search (SHS) and the finding indicated that GWO generated better solutions. A parametric study and sensitivity analysis were conducted using GWO to investigate the effectiveness of different load combinations, beam spans, and slab thicknesses on optimal total cost. The results showed that the optimum cost was sensitive to slab thickness, highlighting the importance of selecting an appropriate thickness in CBs’ design.