Resource Optimisation Method for Multi-Agent Manufacturing System Based on Cloud-Edge Collaboration Architecture

The multi-agent manufacturing system has emerged as a well-established paradigm in intelligent manufacturing. Presently, challenges such as limited adaptability, elevated maintenance expenses, and complexities in enabling local agent deployment at end devices persist. To address such issues, a deployment model for the multi-agent manufacturing system was proposed, leveraging a cloud-edge collaboration architecture. However, managing agents effectively in this environment to establish resilient services, which are services capable of maintaining high availability, stability, and reliability even in the face of uncertainty, emergencies, or failures, for manufacturing systems remains a critical challenge that requires immediate resolution. In the present study, a cloud-edge-end oriented deployment architecture for multi-agent manufacturing system was proposed, and a real-time mapping method between edge agents and production resources based on the 5th generation mobile communication technology is constructed. At the same time, a resource optimisation method called swarm avian evolutionary algorithm is proposed. This method integrates particle swarm optimisation and meta-heuristics to minimise computation time and enhance system response speed. Finally, the proposed resource optimisation method is compared with the genetic algorithm, particle swarm optimisation, and snake optimiser algorithms. The results demonstrate that the convergence time is significantly reduced, indicating that the proposed method offers superior performance.