Dataset Open Access

An Empirical Comparison of Meta-Modeling Techniques for Robust Design Optimization

Sibghat Ullah; Hao Wang; Stefan Menzel; Thomas Bäck; Bernhard Sendhoff


DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="DOI">10.5281/zenodo.3854910</identifier>
  <creators>
    <creator>
      <creatorName>Sibghat Ullah</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-2627-6019</nameIdentifier>
      <affiliation>University of Leiden</affiliation>
    </creator>
    <creator>
      <creatorName>Hao Wang</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-4933-5181</nameIdentifier>
      <affiliation>University of Leiden</affiliation>
    </creator>
    <creator>
      <creatorName>Stefan Menzel</creatorName>
      <affiliation>Honda Research Institute Europe GmBH</affiliation>
    </creator>
    <creator>
      <creatorName>Thomas Bäck</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-6768-1478</nameIdentifier>
      <affiliation>University of Leiden</affiliation>
    </creator>
    <creator>
      <creatorName>Bernhard Sendhoff</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-1233-9584</nameIdentifier>
      <affiliation>Honda Research Institute Europe GmBH</affiliation>
    </creator>
  </creators>
  <titles>
    <title>An Empirical Comparison of Meta-Modeling Techniques for Robust Design Optimization</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>meta-modeling</subject>
    <subject>surrogate-assisted optimization</subject>
    <subject>robust optimization</subject>
    <subject>quality engineering</subject>
    <subject>machine learning</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-02-20</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3854910</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3854909</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/ecole_itn</relatedIdentifier>
  </relatedIdentifiers>
  <version>1</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by-sa/4.0/legalcode">Creative Commons Attribution Share Alike 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;This is the data and source code used in the paper below:&lt;/p&gt;

&lt;p&gt;Sibghat Ullah, Hao Wang, Stefan Menzel, Bernhard Sendhoff and Thomas B&amp;auml;ck, &amp;ldquo;An Empirical Comparison of Meta-Modeling Techniques for Robust Design Optimization&amp;rdquo;, in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 6-9 December 2019, doi:&amp;nbsp;10.1109/SSCI44817.2019.9002805&lt;/p&gt;

&lt;p&gt;This research investigates the potential of using meta-modeling techniques in the context of robust optimization namely optimization under uncertainty/noise. A systematic empirical comparison is performed for evaluating and comparing different meta-modeling techniques for robust optimization. The experimental setup includes three noise levels, six meta-modeling algorithms, and six benchmark problems from the continuous optimization domain, each for three different dimensionalities. Two robustness definitions: robust regularization and robust composition, are used in the experiments. The meta-modeling techniques are evaluated and compared with respect to the modeling accuracy and the optimal function values. The results clearly show that Kriging, Support Vector Machine and Polynomial regression perform excellently as they achieve high accuracy and the optimal point on the model landscape is close to the true optimum of test functions in most cases.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/766186/">766186</awardNumber>
      <awardTitle>Experience-based Computation: Learning to Optimise</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
20
1
views
downloads
All versions This version
Views 2020
Downloads 11
Data volume 1.6 MB1.6 MB
Unique views 1414
Unique downloads 11

Share

Cite as