基于改进灰狼优化算法的防汛物资配送路径规划OA

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.