A variable-fidelity multi-objective optimization method for aerospace structural design optimization

Variable-fidelity (VF) surrogate models have been widespreadly applied to aerospace structural design and optimization problems with multiple objectives to alleviate the optimization cost. To enhance the performance of the existing multi-objective optimization algorithms based on VF surrogate model, a variable-fidelity hypervolume expected improvement (VF-HVEI) method is proposed. Co-Kriging model is utilized to replace computational expensive objective functions in the proposed method, and it is sequentially updated with the VF-HEVI method during the optimization process. The proposed infilling criterion effectively considers the prediction uncertainty of the VF surrogate model, the contribution of sample points of different fidelity on the improvement of the current Pareto front and the computation cost of different simulation models at the same time. The test results in analytical and engineering examples indicate that the proposed method obtains more accurate and robust Pareto front under the same simulation cost.

Automated summary

In this paper, a variable-fidelity hypervolume expected improvement (VF-HVEI) method is proposed to enhance the performance of existing multi-objective optimization algorithms. The method utilizes a Co-Kriging model to replace computationally expensive objective functions and sequentially updates it with the VF-HEVI method during the optimization process. Experimental results demonstrate that the proposed method achieves more accurate and robust Pareto front under the same simulation cost.