A CAD/CAE integrated framework for structural design optimization using sequential approximation optimization

摘要

This paper presents an open and integrated framework that performs the structural design optimization by associating the improved sequential approximation optimization (SAO) algorithm with the CAD/CAE integration technique. In the improved SAO algorithm, a new estimate of the width of Gaussian kernel functions is proposed to enhance the surrogate models for SAO. Based on the improved surrogate models, an adaptive sampling strategy is developed to balance the exploration/exploitation in the sampling process, which better balances between the competence to locate the global optimum and the computation efficiency in the optimization process. Fewer function evaluations are required to seek the optimum, which is of great significance for computation-intensive structural optimization problems. Moreover, based on scripting program languages and Application Programming Interfaces (APIs), integration between commercial CAD and CAE software packages is implemented to expand the applications of the SAO algorithm in mechanical practices. Two benchmark tests from simple to complex, from low-dimension to moderate-dimension were performed to validate the efficacy of the proposed framework. Results show that the proposed approach facilitates the structural optimization process and reduces the computing cost immensely compared to other approaches.