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    A Right-Hand Side Function Surrogate Model-Based Method for the Black-Box Dynamic Optimization Problem

    Source: Journal of Mechanical Design:;2023:;volume( 145 ):;issue: 009::page 91701-1
    Author:
    Zhang, Qi
    ,
    Wu, Yizhong
    ,
    Qiao, Ping
    ,
    Lu, Li
    ,
    Xia, Zhehao
    DOI: 10.1115/1.4062641
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: When solving the black-box dynamic optimization problem (BDOP) in the sophisticated dynamic system, the finite difference technique requires significant computational efforts on numerous expensive system simulations to provide approximate numerical Jacobian information for the gradient-based optimizer. To save computational budget, this work introduces a BDOP solving framework based on the right-hand side (RHS) function surrogate model (RHSFSM), in which the RHS derivative functions of the state equation are approximated by the surrogate models, and the Jacobian information is provided by inexpensive estimations of RHSFSM rather than the original time-consuming system evaluations. Meanwhile, the sampling strategies applicable to the construction of RHSFSM are classified into three categories: direct, indirect, and hybrid sampling strategy, and the properties of these strategies are analyzed and compared. Furthermore, to assist the RHSFSM-based BDOP solving framework search for the optimum efficiently, a novel dynamic hybrid sampling strategy is proposed to update RHSFSM sequentially. Finally, two dynamic optimization examples and a co-design example of a horizontal axis wind turbine illustrate that the RHSFSM-based BDOP solving framework integrated with the proposed dynamic hybrid sampling strategy not only solves the BDOP efficiently but also achieves the optimal solution robustly and reliably compared to other sampling strategies.
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      A Right-Hand Side Function Surrogate Model-Based Method for the Black-Box Dynamic Optimization Problem

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    contributor authorZhang, Qi
    contributor authorWu, Yizhong
    contributor authorQiao, Ping
    contributor authorLu, Li
    contributor authorXia, Zhehao
    date accessioned2023-11-29T19:31:00Z
    date available2023-11-29T19:31:00Z
    date copyright6/15/2023 12:00:00 AM
    date issued6/15/2023 12:00:00 AM
    date issued2023-06-15
    identifier issn1050-0472
    identifier othermd_145_9_091701.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4294830
    description abstractWhen solving the black-box dynamic optimization problem (BDOP) in the sophisticated dynamic system, the finite difference technique requires significant computational efforts on numerous expensive system simulations to provide approximate numerical Jacobian information for the gradient-based optimizer. To save computational budget, this work introduces a BDOP solving framework based on the right-hand side (RHS) function surrogate model (RHSFSM), in which the RHS derivative functions of the state equation are approximated by the surrogate models, and the Jacobian information is provided by inexpensive estimations of RHSFSM rather than the original time-consuming system evaluations. Meanwhile, the sampling strategies applicable to the construction of RHSFSM are classified into three categories: direct, indirect, and hybrid sampling strategy, and the properties of these strategies are analyzed and compared. Furthermore, to assist the RHSFSM-based BDOP solving framework search for the optimum efficiently, a novel dynamic hybrid sampling strategy is proposed to update RHSFSM sequentially. Finally, two dynamic optimization examples and a co-design example of a horizontal axis wind turbine illustrate that the RHSFSM-based BDOP solving framework integrated with the proposed dynamic hybrid sampling strategy not only solves the BDOP efficiently but also achieves the optimal solution robustly and reliably compared to other sampling strategies.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleA Right-Hand Side Function Surrogate Model-Based Method for the Black-Box Dynamic Optimization Problem
    typeJournal Paper
    journal volume145
    journal issue9
    journal titleJournal of Mechanical Design
    identifier doi10.1115/1.4062641
    journal fristpage91701-1
    journal lastpage91701-17
    page17
    treeJournal of Mechanical Design:;2023:;volume( 145 ):;issue: 009
    contenttypeFulltext
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