人民长江2026，Vol.57Issue(7)：9-18,10.DOI:10.16232/j.cnki.1001-4179.2026.07.002
基于改进灰狼优化算法的防汛物资配送路径规划
Flood control materials distribution path planning based on improved grey wolf optimization algorithm
摘要
Abstract
To ensure the efficiency of flood control and rescue operations,it is essential to deliver flood control materials quickly and accurately to widely distributed areas where floods have occurred or where potential risks exist.This paper proposes a multi-strategy improved grey wolf optimization algorithm(MIGWO).The algorithm integrates four improved strategies:a Standard chaot-ic mapping and lens opposition-based learning initialization strategy to enhance the quality of the initial population;an adaptive convergence factor to balance global and local search capabilities;a sequential crossover improvement strategy to enhance informa-tion exchange efficiency;and a hybrid local search strategy to avoid falling into local optima.These strategies collectively enhance the overall performance of the algorithm.Tested on the standard TSPLIB benchmark dataset,MIGWO demonstrates faster conver-gence speed and higher accuracy compared with five other mainstream swarm intelligence algorithms.Furthermore,in a real-world case involving the rapid delivery of various flood control materials to 30 reservoirs under emergency conditions,for node scales of 20 and 30,the optimal delivery times planned by this algorithm are shortened by9.02%and6.27%,respectively,than those of the traditional grey wolf optimization algorithm.These results indicate that the proposed algorithm can significantly im-prove emergency response speed and contribute effectively to flood control and rescue efforts.关键词
改进灰狼优化算法/配送路径规划/仿真实验分析/Standard映射/混合局部搜索/抗洪抢险Key words
improved grey wolf optimization algorithm/distribution route planning/simulation experiment analysis/Standard mapping/hybrid local search/flood control and rescue分类
建筑与水利引用本文复制引用
朱斌,李学昆,涂一凡,李洪钢,史赟..基于改进灰狼优化算法的防汛物资配送路径规划[J].人民长江,2026,57(7):9-18,10.基金项目
国家重点研发计划项目(2023YFD2401800) （2023YFD2401800）
江西省重点研发计划项目(20261BCF320017) （20261BCF320017）
