发表机构
Institute of Turbomachinery, Xi’an Jiaotong University（西安交通大学涡轮机研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多保真度优化中的样本分配问题，提出Filter-GEI填充准则，通过自适应滤波函数平衡局部与全局搜索，并支持并行填充多个样本，在数学与工程问题中验证了有效性。
AI 中文摘要
多保真度优化（MFO）在工程设计领域受到广泛关注，它通过利用大量低保真度（LF）但廉价的样本补充少量昂贵的高保真度（HF）样本，以提升优化性能。影响MFO有效性的一个关键因素是如何在迭代过程中自适应地为HF和LF模拟分配样本。为解决MFO中的样本分配问题，我们提出了一种新的填充准则，名为Filter-GEI，该准则在广义期望改进（GEI）采集函数之上施加了一个自适应滤波函数。具体而言，通过考虑HF与LF模型之间的相关性，Filter-GEI能够高效地分配HF和LF样本，以在局部搜索与全局搜索之间取得良好平衡。此外，考虑到并行计算，Filter-GEI在每次迭代中填充多个HF和LF样本，随着计算能力的提升，这可以进一步提高其效率。通过在五个数学玩具问题和一个涡轮叶片设计工程问题上的测试，所提算法的有效性得到了充分验证。
英文摘要
Multi-Fidelity optimization (MFO) has received extensive attentions in engineering design, which resorts to augmenting the small number of expensive high-fidelity (HF) samples by a large number of low-fidelity (LF) but cheap samples to improve the optimization performance. A key factor that influences the effectiveness of MFO is how to adaptively assign samples for HF and LF simulations in the iteration process. To address such sample assignment issue in MFO, we propose a new infill criterion named as Filter-GEI, which imposes an adaptive filter function on top of the generalized expected improvement (GEI) acquisition function. In particular, by taking the correlations between HF and LF models into account, the Filter-GEI can efficiently allocate HF and LF samples to achieve a good balance in between the local and global search. Furthermore, considering parallel computing, the Filter-GEI infills multiple HF and LF samples in each iteration, which can further improve its efficiency as computing power increases. Through tests on five mathematical toy problems and one engineering problem for the turbine blade design, the effectiveness of the proposed algorithm has been well demonstrated.
Comments13 pages, 6 figures
Journal refStructural and Multidisciplinary Optimization 64(3), 1457-1468 (2021)
DOI：10.1007/s00158-021-02931-1
