Using of Kriging Surrogate Model in the Multi-Objective Optimization of Complicated Structure

被引:0
|
作者
Liu, Lei [1 ]
Ma, Aijun [1 ]
Liu, Hongying [1 ]
机构
[1] China Astronaut Res & Training Ctr, Space Environm Simulat Lab, Beijing 100094, Peoples R China
来源
PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON STRUCTURAL, MECHANICAL AND MATERIAL ENGINEERING (ICSMME 2015) | 2016年 / 19卷
关键词
Kriging surrogate model; multi-objective optimization; complicated structure; design of experiment; sample points; Pareto set;
D O I
暂无
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
To solve the problem of too large calculating amount in the multi-objective optimization of complicated structure, a method based on Kriging surrogate model is proposed and being used in an aerospace assembly to verify its effectiveness. There are two objectives in the structural optimization of the assembly, min mass and max frequency. The method is based on the following steps, first get the sample point by the design of the experiment, and then create the Kriging surrogate model based on the sample points, and at last get the Pareto set by using multi-objective genetic algorithm method working on the established surrogate model to solve the multi-objective problem. The designer can choose the suitable Pareto solution to modify the structure. The method can save the design time and cost at the same time and can provide a reference for similar products.
引用
收藏
页码:203 / 206
页数:4
相关论文
共 50 条
  • [1] Multi-objective optimization of coronary stent using Kriging surrogate model
    Li, Hongxia
    Gu, Junfeng
    Wang, Minjie
    Zhao, Danyang
    Li, Zheng
    Qiao, Aike
    Zhu, Bao
    BIOMEDICAL ENGINEERING ONLINE, 2016, 15
  • [2] Multi-objective optimization of coronary stent using Kriging surrogate model
    Hongxia Li
    Junfeng Gu
    Minjie Wang
    Danyang Zhao
    Zheng Li
    Aike Qiao
    Bao Zhu
    BioMedical Engineering OnLine, 15
  • [3] A Generative Kriging Surrogate Model for Constrained and Unconstrained Multi-objective Optimization
    Hussein, Rayan
    Deb, Kalyanmoy
    GECCO'16: PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 2016, : 573 - 580
  • [4] Multi-Objective Optimization for Structure Crashworthiness Based on Kriging Surrogate Model and Simulated Annealing Algorithm
    Sun X.
    Wang D.
    Li R.
    Zhang B.
    Journal of Shanghai Jiaotong University (Science), 2020, 25 (06) : 727 - 738
  • [5] Multi-Objective Optimization of Bioresorbable Magnesium Alloy Stent by Kriging Surrogate Model
    Wang, Hongjun
    Jiao, Li
    Sun, Jie
    Yan, Pei
    Wang, Xibin
    Qiu, Tianyang
    CARDIOVASCULAR ENGINEERING AND TECHNOLOGY, 2022, 13 (06) : 829 - 839
  • [6] Multi-Objective Optimization of Bioresorbable Magnesium Alloy Stent by Kriging Surrogate Model
    Hongjun Wang
    Li Jiao
    Jie Sun
    Pei Yan
    Xibin Wang
    Tianyang Qiu
    Cardiovascular Engineering and Technology, 2022, 13 : 829 - 839
  • [7] On Multi-Objective Efficient Global Optimization Via Universal Kriging Surrogate Model
    Palar, Pramudita Satria
    Shimoyama, Koji
    2017 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2017, : 621 - 628
  • [8] Multi-objective reliability based design optimization using Kriging surrogate model for cementless hip prosthesis
    Dammak, Khalil
    El Hami, Abdelkhalak
    COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING, 2020, 23 (12) : 854 - 867
  • [9] Robust Multi-Objective Optimization for Gas Turbine Operation Based on Kriging Surrogate Model
    Xia, Hao
    Jia, Peilin
    Ma, Liang
    2021 PROCEEDINGS OF THE 40TH CHINESE CONTROL CONFERENCE (CCC), 2021, : 6704 - 6709
  • [10] MULTI-OBJECTIVE OPTIMIZATION ON SUPERCRITICAL CO2 RECOMPRESSION BRAYTON CYCLE USING KRIGING SURROGATE MODEL
    Sun, Lei
    Wang, Chongyu
    Zhang, Di
    THERMAL SCIENCE, 2017, 21 : S309 - S316