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An adaptive space preselection method for the multi-fidelity global optimization

机译：多保真全局优化的自适应空间预选方法

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摘要

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.
机译：多保真度（MF）元模型已经适用于航空航天结构优化问题，以缓解计算负担。然而，大多数现有的多保真度优化方法探讨了整个设计空间中的最佳选择，这可能导致优化搜索的低效率。本文提出了一种基于空间预选的多保真度较低置信度限制（SPMF-LCB）优化方法来解决这个问题。首先，提出了一种引导辅助区域选择算法，其可以自适应地分配整个设计空间并选择最潜在的区域以便于优化过程。其次，较低的置信度限制（LCB）方法扩展到多保真水平，其可以通过考虑低保真度（LF）模拟预算来自适应地确定样本点的位置和样本点的位置。最后，可行（POF）方法的概率与扩展的LCB方法组合以处理受约束的优化问题。利用八个分析示例和导弹弧度的优化问题来说明所提出的SPMF-LCB方法的效率。将所提出的方法的性能与四种现有方法进行比较。结果表明，考虑效率和稳健性，所提出的SPMF-LCB方法表现最佳。 （c）2021 Elsevier Masson SAS。版权所有。

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  • 来源
    《Aerospace science and technology》 |2021年第6期|106728.1-106728.18|共18页
  • 作者

    Wu Yuda; Lin Quan; Zhou Qi; Hu Jiexiang; Wang Shengyi; Peng Yutong;

  • 作者单位

    Huazhong Univ Sci & Technol Sch Aerosp Engn Wuhan 430074 Hubei Peoples R China;

    Huazhong Univ Sci & Technol Sch Aerosp Engn Wuhan 430074 Hubei Peoples R China;

    Huazhong Univ Sci & Technol Sch Aerosp Engn Wuhan 430074 Hubei Peoples R China;

    Huazhong Univ Sci & Technol Sch Aerosp Engn Wuhan 430074 Hubei Peoples R China;

    Huazhong Univ Sci & Technol Sch Aerosp Engn Wuhan 430074 Hubei Peoples R China;

    Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Multi-fidelity metamodel; Space preselection; Bootstrap; Constrained optimization; Lower confidence bounding;

    机译：多保真元模型;太空预选;举止;受限优化;较低的置信度;

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