With the rapid development of the industrial Internet, multi-machine collaborative control has become a key technology for enhancing the efficiency and stability of industrial systems. Traditional optimization methods are difficult to meet its requirements of high dimension, nonlinearity and real-time performance. Therefore, this study proposes an intelligent algorithm-based optimization strategy for multi-machine collaborative control, implemented through a PSO-DDPG hybrid framework combining Particle Swarm Optimization (PSO) and Deep Deterministic Policy Gradient (DDPG). This method combines the global search ability of PSO and the local fine tuning of DDPG, and solves the optimization problem in the dynamic control environment through adaptive strategy adjustment. The experimental results show that PSO-DDPG performs outstandingly in control accuracy, system stability and computational efficiency compared with traditional algorithms (such as PSO, DQN and GA) in multi-machine cooperative control. Specifically, PSO-DDPG has improved control accuracy by 9.8%, reduced response time by 12.3%, reduced energy consumption by 15.2%, and improved computational efficiency by 22%, respectively. Especially in complex environments such as load fluctuations and equipment failures, PSO-DDPG can adjust the control strategy in real time, significantly improving resource utilization and the adaptability of the system. In addition, PSO-DDPG also has significant advantages in reducing computing resource consumption and ensuring system stability, providing an efficient and reliable solution for dynamic optimization tasks in the industrial Internet.
