城 市 低 空 立 体 物 流 网 络 双 种 群 协 同 优 化 方 法

Urban Unmanned Aerial Vehicle（UAV）logistics is a significant application for the low-altitude economy，and the Urban Air Logistics（UAL）network is a critical infrastructure for achieving efficient drone delivery. This paper comprehensively considers critical urban factors such as noise constraints，economic costs，and ground safety risks to investigate optimization methodologies for urban air logistics networks. We propose a novel multiobjective mixed-integer programming model that simultaneously minimizes operational costs and ground safety risks while strictly under noise constraints. A dual-population coevolutionary optimization algorithm is developed，which enables knowledge transfer through individual interactions between populations，effectively enhancing the optimization capability of the algorithm in irregular solution spaces. Computational experiments show that the proposed algorithm outperforms existing methods with performance improving by over 20% on average. The designed multi-layered network achieves a balanced optimum in terms of cost，ground safety risk，and noise.