A control method for center-of-gravity deviation in locomotive bogies based on an improved Grey Wolf Optimization algorithm - Zhu, Renzun; Chen, Jinhe; Li, Simin (Frontiers in Mechanical Engineering, 2025)
As high-speed rail networks continue to expand, the workload for train maintenance has risen correspondingly, and the conventional experience−based manual adjustment of spring compression during bogie overhauls introduces significant uncertainty and safety risks. To address this challenge, we develop a theoretical model for static spring-load adjustment in two-axle railway vehicles, applicable to all four-axle bogie configurations, including locomotives, urban metro cars, high-speed passenger units, and freight wagons. By idealizing the bogie as a planar rigid body, we derive a coupling matrix that relates the loads among the springs. To solve this model, we propose an enhanced Grey Wolf Optimizer (S-GWO) designed to rapidly and accurately identify the optimal adjustment strategy. Specifically, S-GWO introduces three key enhancements to the standard Grey Wolf Optimizer: a Gaussian-distributed nonlinear convergence factor that promotes extensive global exploration in early iterations and rapid, precise convergence in later stages, thereby improving both speed and accuracy; an adaptive learning and exploration scheme that strengthens global search capabilities; and a Cauchy perturbation mechanism applied to the α-wolf, which effectively balances local search refinement with global jumping behavior. We validate the algorithm’s performance by benchmarking S-GWO against several state-of-the-art metaheuristics on twelve classical test functions and the engineering spring function, employing rank-sum tests to confirm the superiority of our enhancements. An ablation study is conducted to isolate and quantify the independent contributions of each proposed modification. We apply the model to the CRH2 bogie parameters and compare S-GWO’s performance with that of several widely cited optimization algorithms. Experimental results demonstrate that S-GWO offers significant advantages in convergence speed, solution accuracy, practicality of shim placement schemes, and robustness. These improvements further enhance the efficiency of controlling static bogie center-of-gravity deviations. This study thus provides robust technical support for precise center-of-gravity adjustment and prediction in four-axle rail vehicles.