聊城大学学报（自然科学版）2026，Vol.39Issue(1)：32-43,12.DOI:10.19728/j.issn1672-6634.2025040010
多代理强化学习驱动遗传算法求解软时间窗电动车路径规划问题
Multi-agent reinforcement learning-driven genetic algorithm for solving the soft time window electric vehicle routing problem
摘要
Abstract
For the path optimization problem considering constraints such as the energy consumption of e-lectric vehicles,order time windows,and vehicle loads,a mixed-integer programming model with the min-imization of path cost as the core optimization objective is first constructed.Then,based on the problem characteristics,a multi-agent reinforcement learning-driven genetic algorithm(MRLGA)is proposed for solving it.In the MRLGA,the multi-agent reinforcement learning method is used to dynamically optimize the mutation and crossover probabilities in the genetic algorithm and intelligently proxy the selection and crossover operations to enhance the algorithm's search efficiency.By introducing the Shannon diversity in-dex to measure population diversity,premature convergence is avoided,and effective maintenance of popu-lation diversity is achieved.The 2-opt search algorithm is adopted to enhance local search capabilities,and the discrete Lévy flight strategy is introduced to improve global search capabilities,achieving efficient ve-hicle path planning.Finally,experiments are conducted with 120 test cases,and the results show that the proposed MRLGA algorithm can effectively reduce path costs under complex constraints,verifying the ef-fectiveness and feasibility of the algorithm.关键词
车辆路径问题/遗传算法/香农多样性指数/强化学习Key words
vehicle routing problem/genetic algorithm/shannon diversity index/reinforcement learning分类
交通工程引用本文复制引用
HAN Yuyan,AN Junyu,YANG Xiaoyu,WANG Yuting,LI Huan,TIAN Xinru..多代理强化学习驱动遗传算法求解软时间窗电动车路径规划问题[J].聊城大学学报（自然科学版）,2026,39(1):32-43,12.基金项目
国家自然科学基金项目(61973203,61803192,62106073,61966012) （61973203,61803192,62106073,61966012）
山东省自然科学基金项目(ZR2023MF022,ZR2024MF112) （ZR2023MF022,ZR2024MF112）
聊城大学光岳青年创新团队项目(LCUGYTD2022-03)资助 （LCUGYTD2022-03）
