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A new hybrid evolutionary-based method combining particle swarm algorithm and chaotic search is proposed for optimizing the secondary cooling process in continuous casting of steel. This method is employed to explore the space parameter settings to minimize a cost function related to the quality of cast billets and the process feasibility. Particularly, the cost function is evaluated with the aid of a heat transfer and solidification model based on finite element method. And the cost function is non-linear and non-differentiable due to the phase changes during the solidification process, which is difficult to most of traditional methods. Therefore, to achieve high performance in optimizing cooling condition for defect-free products, the chaotic search mechanism is embedded in the standard particle swarm algorithm adaptively to avoid the stagnancy and increase the speed of convergence. This hybrid method makes use of the ergodicity of chaotic search to improve the capability of precise search and keep the balance between the global search and the local search, which has been compared with other methods such as standard particle swarm algorithm, standard genetic algorithm and improved particle swarm algorithm. In comparison, the proposed method shows its superiority in convergence property and robustness. It is validated by the simulation results on both benchmark problems and industrial implementation.