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Simultaneous distribution and sizing optimization for stiffeners with an improved genetic algorithm with two-level approximation.

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Title: Simultaneous distribution and sizing optimization for stiffeners with an improved genetic algorithm with two-level approximation.
Authors: Chen, Shenyan1 (AUTHOR), Dong, Tianshan1 (AUTHOR) tsdong@buaa.edu.cn, Shui, Xiaofang1 (AUTHOR)
Source: Engineering Optimization. Nov2019, Vol. 51 Issue 11, p1845-1866. 22p.
Subjects: Approximation algorithms, Topology
Abstract: This article presents an improved genetic algorithm with two-level approximation (GATA) to optimize the distribution and size of stiffeners simultaneously. A novel optimization model of stiffeners, including two kinds of design variables, is established. The first level approximation problem transforms the original implicit problem to an explicit problem which involves the topology and size variables. Then, a genetic algorithm (GA) addresses the mixed variables. The individuals in the GA are coded by topology variables, and when calculating an individual's fitness, the second level approximation problem is embedded to optimize the size variables. Considering the stiffeners' optimization, several aspects of the initial GATA are updated, including the relationship between two kinds of variables, the weight and its sensitivity calculation and the GA strategy, to optimize the stiffeners' size and distribution simultaneously. Numerical examples show that the improved GATA is effective in optimizing the stiffened shells' topology and size variables simultaneously. [ABSTRACT FROM AUTHOR]
Copyright of Engineering Optimization is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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An: 138454764
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  Data: Simultaneous distribution and sizing optimization for stiffeners with an improved genetic algorithm with two-level approximation.
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  Data: <searchLink fieldCode="AR" term="%22Chen%2C+Shenyan%22">Chen, Shenyan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dong%2C+Tianshan%22">Dong, Tianshan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> tsdong@buaa.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Shui%2C+Xiaofang%22">Shui, Xiaofang</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: This article presents an improved genetic algorithm with two-level approximation (GATA) to optimize the distribution and size of stiffeners simultaneously. A novel optimization model of stiffeners, including two kinds of design variables, is established. The first level approximation problem transforms the original implicit problem to an explicit problem which involves the topology and size variables. Then, a genetic algorithm (GA) addresses the mixed variables. The individuals in the GA are coded by topology variables, and when calculating an individual's fitness, the second level approximation problem is embedded to optimize the size variables. Considering the stiffeners' optimization, several aspects of the initial GATA are updated, including the relationship between two kinds of variables, the weight and its sensitivity calculation and the GA strategy, to optimize the stiffeners' size and distribution simultaneously. Numerical examples show that the improved GATA is effective in optimizing the stiffened shells' topology and size variables simultaneously. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Engineering Optimization is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/0305215X.2018.1558444
    Languages:
      – Code: eng
        Text: English
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        PageCount: 22
        StartPage: 1845
    Subjects:
      – SubjectFull: Approximation algorithms
        Type: general
      – SubjectFull: Topology
        Type: general
    Titles:
      – TitleFull: Simultaneous distribution and sizing optimization for stiffeners with an improved genetic algorithm with two-level approximation.
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            NameFull: Chen, Shenyan
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            NameFull: Dong, Tianshan
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            NameFull: Shui, Xiaofang
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            – D: 01
              M: 11
              Text: Nov2019
              Type: published
              Y: 2019
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              Value: 51
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              Value: 11
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            – TitleFull: Engineering Optimization
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