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As vehicular networks shoulder an ever-expanding workload, smart task offloading has become essential for enhancing system throughput and maximizing resource efficiency. Nonetheless, the fluid topology, rapid node mobility, and varied user requirements make devising robust offloading schemes a persistent challenge. In this study, we proposes a task offloading method that synthesizes a reinforcement learning algorithm and an evolutionary one i.e., PPO-Enhanced NSGA-III Offloading Algorithm (PENOA). This method leverages a PPO reinforcement learning algorithm for dynamically adjusting offloading schedules produced by an NSGA-III algorithm. PENOA is capable of learning to generate near Pareto-optimal solution sets by collaboratively tuning, computational resource allocation and task schedules strategies between vehicles and servers. Experimental studies based on real-world Taxi Trajectory and Telecom Base Station datasets demonstrate that PENOA outperforms benchmark algorithms across multiple performance metrics.

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PENOA: A Reinforcement Learning-Enhanced Evolutionary Approach for Task Offloading in Vehicular Edge Computing

  • 会议论文

  • 发表日期：

    2026年

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  • Mengfei He,
  • Xiaoning Sun,
  • Yunni Xia,
  • Xifeng Xu,
  • Jiale Zhao,
  • Mengdi Wang

摘要

As vehicular networks shoulder an ever-expanding workload, smart task offloading has become essential for enhancing system throughput and maximizing resource efficiency. Nonetheless, the fluid topology, rapid node mobility, and varied user requirements make devising robust offloading schemes a persistent challenge. In this study, we proposes a task offloading method that synthesizes a reinforcement learning algorithm and an evolutionary one i.e., PPO-Enhanced NSGA-III Offloading Algorithm (PENOA). This method leverages a PPO reinforcement learning algorithm for dynamically adjusting offloading schedules produced by an NSGA-III algorithm. PENOA is capable of learning to generate near Pareto-optimal solution sets by collaboratively tuning, computational resource allocation and task schedules strategies between vehicles and servers. Experimental studies based on real-world Taxi Trajectory and Telecom Base Station datasets demonstrate that PENOA outperforms benchmark algorithms across multiple performance metrics.