基于eGWO的物流车车架轻量化研究

Research on Lightweight of Logistics Vehicle Frame Based on eGWO

为提高物流车车架轻量化环节的收敛速度与精度,提出一种基于警戒者灰狼策略与准反射学习策略的改进型灰狼优化算法(eGWO),通过与其他算法进行测试对比,表明eGWO具有良好的求解性能;对物流车车架进行满载弯曲、满载扭转、满载制动、满载转向工况仿真分析,使用最优拉丁超立方试验设计方法与响应面模型方法建立车架近似模型,并使用eGWO对物流车车架的尺寸优化数学模型进行求解,使得车架总质量下降11.9kg,减重率9.94%,为车架轻量化研究提供了可行方法。

In order to improve the convergence speed and accuracy of lightweight link of logistics vehicle frame,an improved Gray Wolf optimization algorithm(eGWO)based on the Gray Wolf strategy and quasi-reflection learning strategy is proposed.The test results show that eGWO has good solving performance.Simulation analysis of full-load bending,full-load torsion,full-load braking and full-load steering conditions was carried out on the logistics vehicle frame.The optimal Latin Hypercube test design method and response surface model method were used to establish the approximate frame model,and eGWO was used to solve the mathematical model of the size optimization of the logistics vehicle frame.The total mass of the frame was reduced by 11.9kg and the weight reduction rate was 9.94%.It provides a feasible method for the research of lightweight frame.