Reinforcement Learning Controlled Adaptive PSO for Task Offloading in IIoT Edge Computing

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. However, 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 uses swarm intelligence and predictive models to adapt to dynamic factors, including human interactions and environmental changes. Our method improves resource management and service quality, achieving optimal task offloading and resource distribution in IIoT edge computing.