Parameter-Free Jaya Algorithm for Steel Frame Optimization Under Natural Frequency Constraints

The optimal design of steel frame structures subjected to natural frequency constraints is a challenging problem due to its highly nonlinear and computationally demanding nature. In this study, Parameter-Free Jaya Algorithm (PFJA) is proposed for the minimum-weight design of planar and spatial steel frame systems under prescribed natural frequency constraints. The standard Jaya Algorithm (JA), which is based on moving toward the best solution and away from the worst solution, is enhanced by eliminating algorithm-specific control parameters and enabling automatic self-adaptation during the optimization process. The performance of the proposed PFJA is evaluated using three planar and two spatial steel frame benchmark problems previously studied in the literature. All structural analyses and optimization procedures are implemented in MATLAB R2020b, and the optimal designs are independently verified using SAP2000 v20, yielding identical natural frequency values. The results demonstrate that PFJA outperforms the standard JA, modified Jaya algorithm (MJA), and several recently developed metaheuristic algorithms in terms of minimum structural weight, convergence speed, and statistical robustness. These findings confirm the superiority and reliability of the proposed PFJA for frequency-constrained structural optimization problems.