Multidisciplinary design for structural integrity using a collaborative optimization method based on adaptive surrogate modelling

Abstract

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 surrogate models. Finally, the original objective and constraints at the system level and disciplinary level are replaced by their adaptive surrogate models, respectively. Consequently, the formulation of CO based on the adaptive surrogate model can be obtained. In the proposed method, the accuracy of the non-linear region of the response surface can be enhanced efficiently. An engineering structure design problem is introduced to show the effectiveness of the proposed method.

Keywords

• Collaborative optimization
• Adaptive surrogate model
• Latin hypercube sampling
• Kriging method
• Expected improvement function