A Control Method for Center-of-Gravity Deviation in Locomoti ve Bogies Based on an Improved Grey Wolf Optimization Algorithm

Renzun Zhu¹Jinhe Chen^1*Simin Li²

• ¹Tianyou College, East China Jiaotong University, Nanchang, Jiangxi, China, Jiangxi, China
• ²School of Civil Engineering and Architecture, East China Jiaotong University, Nanchang, Jiangxi, China, Jiangxi, China

As highspeed rail networks continue to expand both domestically and internationally, the workload for train maintenance has risen correspondingly, and the conventional experiencebased 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 springload adjustment in twoaxle railway vehicles, applicable to all fouraxle bogie configurations—including locomotives, urban metro cars, highspeed 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 (SGWO) 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;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 SGWO against several stateoftheart metaheuristics on twelve classical test functions and the engineering Spring function, employing ranksum tests to confirm the superiority of our enhancements.An ablation study is conducted to isolate and quantify the independent contributions of each proposed modification.Furthermore, we apply the model to the CRH2 bogie parameters and compare SGWO’s performance with that of several widely cited optimization algorithms.Experimental results demonstrate that SGWO offers significant advantages in convergence speed, solution accuracy, practicality of shimplacement schemes, and robustness.These improvements further enhance the efficiency of controlling static bogiecenterofgravity deviations.This study thus provides robust technical support for precise centerofgravity adjustment and prediction in fouraxle rail vehicles.