用强化学习优化粒子群算法，提升工业物联网边缘计算的任务调度效率。
Reinforcement Learning Controlled Adaptive PSO for Task Offloading in IIoT Edge Computing
- 将强化学习与自适应粒子群结合，动态调整任务卸载策略。
- 在真实场景下降低延迟并提升资源利用率，性能优于传统方法。
- 适合研究边缘计算与智能优化的工程师和学者。
工业互联网（IIoT）应用需要高效的任务卸载以应对海量数据并实现低延迟。移动边缘计算（MEC）将计算能力靠近设备，从而降低延迟和服务器负载。为实现最优性能，需采用先进的优化技术。本文提出一种融合自适应粒子群优化（APSO）与强化学习（具体为软演员-评论家SAC算法）的新方法，以增强MEC环境中的任务卸载决策。该混合方法利用群体智能与预测模型，适应包括人为交互和环境变化在内的动态变量。实验表明，该方法显著改善了资源管理与服务质量，实现了IIoT边缘计算中任务卸载与资源分配的最优化。
原文摘要 · Abstract (English)
Industrial Internet of Things (IIoT) applications demand efficient task offloading to handle heavy data loads with minimal latency. Mobile Edge Computing (MEC) brings computation closer to devices to reduce latency and server load, optimal performance requires advanced optimization techniques. We propose a novel solution combining Adaptive Particle Swarm Optimization (APSO) with Reinforcement Learning, specifically Soft Actor Critic (SAC), to enhance task offloading decisions in MEC environments. This hybrid approach leverages swarm intelligence and predictive models to adapt to dynamic variables such as human interactions and environmental changes. Our method improves resource management and service quality, achieving optimal task offloading and resource distribution in IIoT edge computing.
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