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As one of the multidisciplinary design optimization methods, the collaborative optimization (CO) can enjoy a high degree of discipline autonomy. It has received extensive attention and been used in the modern engineering design process. However, the original CO is low optimization accuracy and efficiency for the design of high-dimensional nonlinearity systems because of the involved compatibility constraints. To solve this problem, an enhanced CO method based on the adaptive surrogate model is proposed in this study. The strategy of the proposed method includes the following steps. Firstly, the traditional surrogate models of system objective and performance function are constructed by the Latin hypercube sampling and the Kriging method. Then, the expected improvement function and the self-cycling optimization strategy are utilized to modify the traditional surrogate models into the corresponding adaptive …