.png){kind=icon}
返回
An adaptive space preselection method for the multi-fidelity global optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ast.2021.106728.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multi-fidelity (MF) metamodels have been well applied to aerospace structure optimization problems to relieve the computation burden. However, most of the existing multi-fidelity optimization methods explore the optimum in the whole design space, which may lead to low-efficiency of the optimization search. In this paper, a space preselection-based multi-fidelity lower confidence bounding (SPMF-LCB) optimization method is proposed to solve this problem. Firstly, a bootstrap-assisted area selection algorithm is proposed, which can adaptively partition the whole design space and select the most potential area to facilitate the optimization process. Secondly, the lower confidence bounding (LCB) method is extended to the multi-fidelity level, which can adaptively determine both the fidelity level and the location of sample points, with the consideration of the low-fidelity (LF) simulations budget. Finally, the probability of feasible (POF) method is combined with the extended LCB method to handle the constrained optimization problems. Eight analytical examples and the optimization problem of the radome of the missile are utilized to illustrate the efficiency of the proposed SPMF-LCB method. The performance of the proposed approach is compared with four existing methods. Results show that the proposed SPMF-LCB method performs the best considering the efficiency and robustness. (C) 2021 Elsevier Masson SAS. All rights reserved.
Keyword:
Multi-fidelity metamodel
Space preselection
Bootstrap
Constrained optimization
Lower confidence bounding
期刊
IF:
5.8
论文数:
1.0W
被引数:
3.0W
机构
暂无机构信息
引用论文
.png){kind=icon}
Principal component analysis and polynomial chaos expansion for time-variant reliability problems
PREAI
分享收藏
Sequential optimization using multi-level cokriging and extended expected improvement criterion
PREAI
分享收藏Ensemble of surrogate based global optimization methods using hierarchical design space reduction
PREAI
分享收藏
查看更多内容