摘要： 随着物流业务的发展，无人机群协同配送成为降本增效的关键方案。面向传统配送业务的需求及无人机自身的约束，提出一种面向时间窗约束的无人机群绿色协同配送机制。首先，构建多任务点配送场景，设定任务时间窗、任务等级、无人机载重能力与飞行姿态相关能耗等参数，建立以任务收益最大化和能耗最小化为优化目标的多约束模型；然后，通过对斑马优化算法进行离散化处理，使其适应于无人机群路径规划和任务分配的离散问题，设计个体编码规则，引导种群在解空间中进行高效搜索，生成配送方案；最后，在不同任务规模和约束条件下构建仿真环境，对该机制进行系统性测试和对比验证。实验结果表明，所提出的机制在能耗控制、任务收益与收敛速度等方面均显著优于IGCPA、AGA和ACO算法，能够在满足复杂任务约束的同时提升配送效率、降低能源消耗，展现出良好的工程应用前景。

Abstract: With the development of logistics business, the collaborative delivery of unmanned aerial vehicle (UAV) swarms has become a key solution for cost reduction and efficiency improvement. In response to the demands of traditional delivery services and the constraints of UAVs themselves, a green collaborative delivery mechanism for UAV swarms under time window constraints is proposed. Firstly, a multi-task point delivery scenario is constructed, with parameters such as task time windows, task priorities, UAV payload capacity, and flight attitude-related energy consumption set. A multi-constraint model is established with the optimization goals of maximizing task benefits and minimizing energy consumption. Then, by discretizing the Zebra Optimization Algorithm, it is adapted to the discrete problems of UAV swarm path planning and task allocation. An individual coding rule is designed to guide the population to efficiently search in the solution space and generate delivery plans. Finally, simulation environments are built under different task scales and constraint conditions to systematically test and comparatively verify the proposed mechanism. Experimental results show that the proposed mechanism significantly outperforms IGCPA, AGA, and ACO algorithms in terms of energy consumption control, task benefits, and convergence speed. It can enhance delivery efficiency and reduce energy consumption while meeting complex task constraints, demonstrating promising engineering application prospects.