For solving the problems of closed-loop optimization on controller parameters of multiple-controller single-output thermal engineering system, this paper proposes a recurrent optimization method that is based on the particle swarm computing and closed-loop simulation (PSO-RCO). It consists of a set of closed-loop identification, simulation, and optimization functions that are organized in a recurrent working flow. The working flow makes one controller tuned at a time whilst others keep their values. It ends after several rounds of overall optimizations. Such a recurrently alternative tuning can greatly speed up the convergence of controller parameters to reasonable values. Verifications on practical data from a superheated steam temperature control system show that the optimized control system performance is greatly improved by reasonable controller parameters and practicable control action. With the advantage of not interfering system operation and the potential supporting on big data identification method, the PSO-RCO is a promising method for control system optimization.