Abstract: UAV edge-computing systems deploy UAVs as mobile edge servers for cost-effective, low-profile services, but uneven user geography and limited onboard resources make placement critical: misdeployment causes coverage holes, inflated cooperative-communication latency, and load imbalance. We pursue an optimal balance among coverage, communication quality, and energy efficiency by integrating dynamic collaborative offloading with hybrid intelligent algorithms that couple discrete placement with continuous offloading. Tasks are intelligently partitioned with dynamic offloading ratios for real-time load balancing. Subject to latency constraints, we jointly optimize deployment, cooperative offloading, and compute/communication allocation within a nonconvex mixed-integer framework. Placement is handled by a hybrid metaheuristic with adaptive mutation/crossover for faster convergence, while offloading/resource control uses an enhanced DDPG (DP-Hybrid) for coordinated decisions. Simulations demonstrate a superior energy–latency trade-off and substantial reductions in overall system cost versus state-of-the-art baselines.

摘要： 无人机（UAV）边缘计算系统通过将无人机部署为移动边缘服务器，为各大用户提供低成本且具备隐蔽性的服务。然而，由于用户设备在地理位置上的不均匀分布和无人机自身的资源受限，部署不当会导致严重后果：包括用户密集区域出现覆盖盲区造成服务中断、无人机间距离过远导致协作通信时延超过阈值、以及部分无人机负载过重而其他无人机资源闲置的不均衡现象。因此，如何在保证服务覆盖、通信质量与能耗效率之间找到最优平衡，成为亟待解决的核心问题。为此，本文提出基于动态协作卸载任务并且混合智能算法的技术创新来同时解决无人机部署的离散优化问题和任务卸载的连续决策问题。具体而言，本文将计算任务进行智能拆分，通过动态卸载比例实现不同无人机负载的实时平衡，从而提高整体计算效率。在满足延迟约束的前提下，为最小化任务执行时延，本文研究了无人机部署、任务协作卸载以及计算与通信资源分配的联合优化问题，并构建了一个针对非凸混合整数组合优化问题的优化框架。在无人机位置部署方面，采用融合不同算法动态调整变异强度和交叉率的混合智能方法，实现了比传统算法更快的收敛速度；在卸载决策和资源分配方面，提出基于增强型深度确定性策略梯度（DDPG）的DP-Hybrid算法，实现了卸载决策和资源分配的协同优化。仿真实验结果表明，与现有基线方法相比，所提出的算法在能耗与时延之间实现了更优的平衡，显著降低了系统整体成本。