Carbas, SerdarArtar, Musa2024-10-042024-10-0420212352-0124https://doi.org/10.1016/j.istruc.2021.06.096http://hdl.handle.net/20.500.12403/3563In this paper, an unbiased comparative assessment scheme for algorithmic performances of five novel nature-inspired metaheuristic algorithms in design optimization of steel frames made out of cold-formed steel sections under consideration of seismic loading is presented. These contemporary algorithms are so-called tree seed, squirrel search, water strider, grey wolf, and brain storm optimization. The functionality of the proposed algorithms is appraised with respect to design precisions in both portal and space cold-formed steel frames formulated according to the design provisions implemented by AISI-LRFD (American Iron and Steel Institute-Load and Resistance Factor Design). The cross-sectional dimensions of steel profiles, which are selected from available set of cold-formed thin-walled single-C sections, are treated as design variables in the optimization process in order to minimize the structural weight of the frames. In addition to specification constraint requirements, lateral and vertical displacement restrictions of the structural elements required for stability of the frames are also taken into account. Design optimization algorithms necessitate the structural response of cold-formed steel frames under load combinations including seismic loading effects which is accomplished by utilizing the open application programming interface (OAPI) mastery of MATLAB with SAP2000. The design optimization of cold-formed steel frames that is a discrete nonlinear programming problem reveal the robustness and applicability of proposed contemporary nature-inspired metaheuristic algorithms in real-sized complex structural optimization problems.eninfo:eu-repo/semantics/closedAccessCold-formed steel framesStructural optimizationOptimum designNature-inspired metaheuristic algorithmsSeismic loadingAISI-LRFDArticle334011403010.1016/j.istruc.2021.06.0962-s2.0-85110235986Q1WOS:000701936600002Q2