航空学报2026，Vol.47Issue(3)：112-125,14.DOI:10.7527/S1000-6893.2025.31880
面向移动边缘网络的多目标进化深度确定性策略梯度算法
Multi-objective evolution with deep deterministic strategy gradient algorithm for mobile edge networks
摘要
Abstract
The Mobile Edge Computing(MEC)network assisted by Unmanned Aerial Vehicles(UAV)demonstrates great potential in emergency response,real-time monitoring,and other fields.However,the efficient operation of MEC network encounters challenges stemming from multiple optimization objectives,such as high energy consump-tion and high latency.Therefore,a Multi-Objective Evolution with Deep Deterministic Policy Gradient(MOE-DDPG)algorithm for UAV-assisted MEC network optimization is introduced.Firstly,an integrated multi-objective optimization model is established to ensure comprehensive performance of the MEC network by minimizing latency and energy consumption while maximizing the number of completed UAV tasks.Secondly,a bidirectional selection strategy for weight vector and individual matching is proposed to address the difficulty of balancing various objectives in traditional Deep Deterministic Policy Gradient(DDPG)algorithms when dealing with multi-objective optimization problems,thereby significantly enhancing population diversity.Finally,by organically fusing the Multi-Objective Evolution(MOE)algorithm and DDPG algorithm,a novel MOE-DDPG algorithm framework is proposed,which can optimize the overall performance of the MEC network in real time.The experimental results show that the MOE-DDPG algorithm not only significantly improves the distribution and convergence of the Pareto solution set but also effectively reduces energy consumption,latency,and increases the number of completed tasks.关键词
深度强化学习/移动边缘计算/无人机/多目标进化/双向选择Key words
deep reinforcement learning/Mobile Edge Computing(MEC)/unmanned aerial vehicle/Multi-Objective Evolution(MOE)/bidirectional selection分类
航空航天引用本文复制引用
张磊,田灿,文方青,张清河,刘含..面向移动边缘网络的多目标进化深度确定性策略梯度算法[J].航空学报,2026,47(3):112-125,14.基金项目
国家自然科学基金(62271286,62371271,42406173) （62271286,62371271,42406173）
湖北省水电工程智能视觉监测重点实验室开放课题(2024SDSJ02) National Natural Science Foundation of China(62271286,62371271,42406173) （2024SDSJ02）
Open Fund From Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering(2024SDSJ02) （2024SDSJ02）
