Adaptive Sequential Reliability‐Based Design Optimization Using an FGO‐RBF Surrogate Model

ABSTRACT In the context of structural design under uncertainty, this study proposes an adaptive sequential reliability‐based design optimization (RBDO) method using a radial basis function (RBF) surrogate model enhanced by the Fungal Growth Optimizer (FGO). The FGO‐RBF model improves the surrogate's approximation accuracy in high‐dimensional nonlinear design spaces. By combining this model with an adaptive sampling strategy and sequential reliability analysis, the proposed approach reduces the dependence on high‐fidelity finite element models and enhances optimization efficiency while satisfying reliability constraints. The method is validated on multiple benchmark problems, showing improved performance in terms of solution accuracy and reduced true function evaluations. Finally, it is applied to the structural optimization of the roof beam of a portal hydraulic support used in coal mine roadways. Considering load uncertainties and manufacturing tolerances, the method achieves a reliability‐compliant optimal design, demonstrating its practical applicability for engineering structures operating in uncertain environments.