Optimizing the structural design of fixed flexible fall protection devices through NSGA-II and finite element analysis

The structural design of fixed flexible fall protection devices often relies on single-objective optimization methods, leading to uneven stress distribution and local stress concentration, which compromise long-term safety and durability. To address these issues, this study proposes a multi-objective optimization framework integrating the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with finite element analysis (FEA). Key structural parameters influencing load-bearing capacity, stress distribution, and deformation control are identified, forming the basis of a multi-objective optimization model aimed at maximizing bearing capacity, minimizing stress concentration, and optimizing deformation uniformity. To enhance computational efficiency, a Radial Basis Function (RBF)-based surrogate model is developed to approximate FEA results, while parallel computing accelerates population evaluation in NSGA-II. Candidate solutions are validated through FEA simulations, followed by further refinement using adaptive crossover-mutation and local search strategies. The optimized design demonstrates significant performance improvements: average bearing capacity reaches 172.88 kN, with node displacement reduced to 0.97 mm. Deformation gradient uniformity improves to 0.06, and residual deformation ratio decreases to 2.49%. These results confirm that the NSGA-II and FEA-integrated approach effectively enhances mechanical performance and structural reliability. This research not only ensures safer high-altitude operations but also provides a novel, efficient methodology for structural design optimization in related engineering fields.