在互联网电商驱动下，中国快递业务正蓬勃发展，但末端配送环节仍存在显著的资源浪费与效率瓶颈。为解决快递物流“最后1公里”配送中由于路径规划导致的资源浪费问题，提出了一种融合了Logistic映射、自适应大邻域搜索、自适应Metropolis准则和纵横交叉策略的改进灰狼优化算法（adaptive alpha-velocity hybrid chaotic grey wolf optimizer, AAVHCGWO）。消融实验和实际应用实验结果表明，AAVHCGWO相比于原本算法在不同维度的模拟实验中，求解效果和求解效率均有显著提升，在实际配送实验中，AAVHCGWO对比改进遗传算法以及最新提出的成长优化算法和星雀优化算法配送距离分别降低69.0%、54.0%和19.2%。因此，可以得出，AAVHCGWO在模拟实验求解和实际物流配送路径优化问题中均具备有效性、优越性和应用潜力，可以为快递配送行业提供了一种新的优化策略。

The express delivery industry in China is driven by the rapid expansion of internet-based e-commerce. However, significant resource waste and efficiency bottlenecks are observed in the last-mile delivery phase. To address the inefficiencies caused by suboptimal path planning in last-mile logistics, an improved grey wolf optimization algorithm(AAVHCGWO) was proposed. This algorithm integrates logistic mapping, ALNS(adaptive large neighborhood search), an adaptive Metropolis criterion, and a crisscross strategy. Ablation experiments and real-world case studies were conducted to validate the algorithm. Significant improvements in both solution effectiveness and computational efficiency are demonstrated by AAVHCGWO, compared with the original algorithm, across simulation experiments of varying dimensions. Practical delivery trials were performed using the IGA(improved genetic algorithm), GO(growth optimizer), and SOA(starling optimization algorithm). Reductions in delivery distances of 69.0%, 54.0%, and 19.2% were observed in practical trials compared to IGA, GO, and SOA. The effectiveness and superiority of AAVHCGWO are confirmed in both simulated and real-world logistics optimization. This study is considered a novel strategy for enhancing last-mile delivery efficiency.