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A lower confidence bounding approach based on the coefficient of variation for expensive global design optimization

机译：基于变异系数的较低置信度边界方法，用于昂贵的全局设计优化

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摘要

Purpose Engineering design optimization involving computational simulations is usually a time-consuming, even computationally prohibitive process. To relieve the computational burden, the adaptive metamodel-based design optimization (AMBDO) approaches have been widely used. This paper aims to develop an AMBDO approach, a lower confidence bounding approach based on the coefficient of variation (CV-LCB) approach, to balance the exploration and exploitation objectively for obtaining a global optimum under limited computational budget.Design/methodology/approach In the proposed CV-LCB approach, the coefficient of variation (CV) of predicted values is introduced to indicate the degree of dispersion of objective function values, while the CV of predicting errors is introduced to represent the accuracy of the established metamodel. Then, a weighted formula, which takes the degree of dispersion and the prediction accuracy into consideration, is defined based on the already-acquired CV information to adaptively update the metamodel during the optimization process.Findings Ten numerical examples with different degrees of complexity and an AIAA aerodynamic design optimization problem are used to demonstrate the effectiveness of the proposed CV-LCB approach. The comparisons between the proposed approach and four existing approaches regarding the computational efficiency and robustness are made. Results illustrate the merits of the proposed CV-LCB approach in computational efficiency and robustness.Practical implications The proposed approach exhibits high efficiency and robustness in engineering design optimization involving computational simulations.Originality/value CV-LCB approach can balance the exploration and exploitation objectively.
机译：目的涉及计算仿真的工程设计优化通常是耗时的，甚至在计算上过于繁琐。为了减轻计算负担，基于适应性元模型的设计优化（AMBDO）方法已被广泛使用。本文旨在开发一种AMBDO方法，一种基于变异系数（CV-LCB）方法的较低置信度边界方法，以平衡勘探和开发目的，以便在有限的计算预算下获得全局最优值。设计/方法/方法在提出的CV-LCB方法中，引入预测值的变异系数（CV）表示目标函数值的离散程度，同时引入预测误差的CV表示已建立的元模型的准确性。然后，基于已经获取的CV信息定义加权公式，该公式考虑了分散程度和预测精度，以在优化过程中自适应更新元模型。 AIAA空气动力学设计优化问题用于证明所提出的CV-LCB方法的有效性。在计算效率和鲁棒性方面，对提出的方法和四种现有方法进行了比较。实验结果表明了该方法在计算效率和鲁棒性方面的优势。实际意义该方法在涉及计算仿真的工程设计优化中具有很高的效率和鲁棒性。原始/价值CV-LCB方法可以客观地平衡勘探与开发。

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  • 来源
    《Engineering Computations》 |2019年第3期|830-849|共20页
  • 作者

    Cheng Ji; Jiang Ping; Zhou Qi; Hu Jiexiang; Yu Tao; Shu Leshi; Shao Xinyu;

  • 作者单位

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

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

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

    Huazhong Univ Sci & Technol, Sch Mech Sci & Engn, State Key Lab Digital Mfg Equipment & Technol, Wuhan, Hubei, Peoples R China|Georgia Inst Technol, Atlanta, GA 30332 USA;

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

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

    Huazhong Univ Sci & Technol, Sch Mech Sci & Engn, State Key Lab Digital Mfg Equipment & Technol, Wuhan, 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

    Kriging; Coefficient of variation; Lower confidence bounding; Metamodel-based design optimization;

    机译：克里格法;变异系数;较低置信度边界;基于模型的设计优化;

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