A multi-fidelity surrogate model based on design variable correlations

Abstract

Multi-fidelity surrogate (MFS) models have garnered significant attention in the field of engineering optimization due to their ability to attain the desired accuracy at a reduced cost. However, most previous MFS models employed a single scaling factor for the global design space, which posed challenges in adaptively adjusting the scaling factor within local design spaces. To address this issue, this paper proposes a novel MFS model based on design variable correlations (MFS-DVC). MFS-DVC introduces local characteristics to the scaling factors by leveraging correlations between design variables, enabling adaptive adjustments of the scaling factors at different positions within the design space. Moreover, MFS-DVC offers a more comprehensive exploration of relationships and information among high-fidelity (HF) data points by utilizing the correlations between design variables. This enhancement contributes to improving the accuracy and robustness of the model. The performance of MFS-DVC was evaluated by comparing it with three benchmark MFS models and two single-fidelity surrogate models on 22 test functions and one engineering problem involving a hydraulic press. Additionally, the cost ratio and combination of HF and low-fidelity samples were studied to assess their impact on the performance of the MFS-DVC model. The results demonstrate that MFS-DVC consistently delivers competitive performance in terms of prediction accuracy and robustness.