The mobile edge computing network assisted by unmanned aerial vehicles , demonstrates great potential in emergency response, real-time monitoring, and other fields. However, the efficient operation of MEC networks en-counters challenges stemming from multiple optimization objectives, such as high energy consumption and high latency. Therefore, this paper introduces a Multi-Objective Evolution with Deep Deterministic Policy Gradient (MOE-DDPG) algorithm for UAV-assisted MEC network optimization. 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 DDPG algorithms when dealing with multi-objective optimization problems, thereby significantly enhancing population diversity. Finally, by organically fusing the 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 simultaneously.
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