Lightweight Design of Steel Truss Structures Based on a Hybrid Strategy-Enhanced Snow Geese Optimization Algorithm

Structural optimization of steel trusses in mining areas is challenged by complex constraints, heavy loads, and the need for rigorous lightweight design. To address these issues, this study proposes a Multi-Strategy Enhanced Snow Geese Algorithm (AUR-SGA). While the standard Snow Geese Optimization Algorithm (SGA) shows potential, it often struggles with local optima entrapment and slow convergence in high-dimensional structural problems. To overcome these limitations, AUR-SGA integrates three enhancements: an adaptive nonlinear factor for global search, an elite decentralization strategy for population diversity, and a golden sine mechanism to balance exploration and exploitation. The algorithm’s performance is rigorously evaluated using fifteen benchmark functions and specific strategy validation tests. Further experiments on CEC2017 and CEC2022 test sets demonstrate that AUR-SGA significantly outperforms competitors, with statistical significance confirmed by Wilcoxon rank-sum and Friedman tests. Crucially, the practical engineering value of AUR-SGA is demonstrated through a 15-variable cantilever beam and a real-world coal mine transfer trestle optimization project. Results indicate that the proposed method achieves average structural weight reductions of 31.73% and 21.14%, respectively, compared to competitors. These findings confirm that AUR-SGA provides a robust, high-precision solution for the cost-effective design of complex industrial steel structures.