Abstract: An adaptive algorithm was improved and introduced to the particle swarm optimization algorithm (PSO). When the population evolution reaches certain generations, the search area is changed in accordance with the improved adaptive algorithm. It is achieved that the search area is revised automatically to increase the convergence rate and precision and prevent the premature convergence of the particle swarm algorithm. The conclusion was verified through simulation. Finally, the new algorithm was applied to the optimum design of scheduling hot strip mills, whose running time was less than 5 s, which validated the real-time application to provide an effective way to optimize.