A Surrogate-assisted Multi-objective Grey Wolf Optimizer for Empty-heavy Train Allocation Considering Coordinated Line Utilization Balance  

作　　者：Zhigang Du Shaoquan Ni Jeng-Shyang Pan Shuchuan Chu 

机构地区：[1]School of Transportation and Logistics,Southwest Jiaotong University,Chengdu,610031,China [2]School of Artificial Intelligence,Nanjing University of Information Science and Technology,Nanjing,210044,China [3]National and Local Joint Engineering Lab for Integrated Transportation Intelligence,Chengdu,610031,China [4]National Engineering Lab of Comprehensive Transportation Big Data Application Technology,Chengdu,610031,China

出　　处：《Journal of Bionic Engineering》2025年第1期383-397,共15页仿生工程学报(英文版)

基　　金：supported by the National Natural Science Foundation of China(Project No.52172321;52102391);Sichuan Province Science and Technology Innovation Talent Project(2024JDRC0020);China Shenhua Energy Company Limited Technology Project(GJNY-22-7/2300-K1220053);Key science and technology projects in the transportation industry of the Ministry of Transport(2022-ZD7-132).

摘　　要：This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balance.By integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization process.The effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective algorithms.Furthermore,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy trains.The research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.

关 键 词：Surrogate-assisted model Grey wolf optimizer Multi-objective optimization Empty-heavy train allocation 

分 类 号：O17[理学—数学]