A hybrid reinforcement learning and Harris Hawks Optimization framework for multi-objective resource management in IIoT

The Industrial Internet of Things (IIoT) is revolutionizing industrial processes by interconnecting sensors, machines, and computing resources to enable intelligent automation, real-time monitoring, and predictive maintenance. Despite its potential, efficient allocation and management of resources in IIoT networks remain a significant challenge due to the dynamic, heterogeneous, and large-scale nature of industrial environments. Traditional static or heuristic resource allocation methods often fail to satisfy conflicting objectives such as minimizing latency, maximizing throughput, ensuring energy efficiency, and maintaining system reliability. To address these challenges, this paper presents the design and development of an AI-based multi-objective resource management system for IIoT systems. The system integrates reinforcement learning (RL) for adaptive, real-time decision-making with Harris Hawks Optimization (HHO) for multi-objective optimization, balancing competing performance metrics across sensing, communication, computing, and control layers. The RL agent predicts system states and dynamically adjusts resource allocation, while HHO fine-tunes the solutions to achieve optimal performance under multiple constraints. Simulation studies demonstrate that the proposed system outperforms conventional static and heuristic approaches, achieving higher efficiency, faster adaptability, and improved scalability. By intelligently managing resources and optimizing system behavior, this system enhances overall operational reliability and Quality-of-Service in IIoT networks. The results indicate that combining AI-driven decision-making with advanced nature-inspired optimization provides a robust and efficient solution for next-generation industrial applications, paving the way for more intelligent, resilient, and resource-efficient IIoT systems.