Dataset Open Access

Computational Study on Effectiveness of Knowledge Transfer in Dynamic Multi-objective Optimization

Gan Ruan; Leandro L. Minku; Stefan Menzel; Bernhard Sendhoff; Xin Yao


MARC21 XML Export

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
  <leader>00000nmm##2200000uu#4500</leader>
  <datafield tag="041" ind1=" " ind2=" ">
    <subfield code="a">eng</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Evolutionary algorithms</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">transfer learning</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">dynamic multi-objective optimization</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">prediction-based method</subfield>
  </datafield>
  <controlfield tag="005">20200527202032.0</controlfield>
  <controlfield tag="001">3859701</controlfield>
  <datafield tag="711" ind1=" " ind2=" ">
    <subfield code="d">19-24 July 2020</subfield>
    <subfield code="g">IEEE CEC</subfield>
    <subfield code="a">2020 IEEE Congress on Evolutionary Computation</subfield>
    <subfield code="c">Glasgow, UK</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">University of Birmingham</subfield>
    <subfield code="0">(orcid)0000-0002-2639-0671</subfield>
    <subfield code="a">Leandro L. Minku</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Honda Research Institute Europe GmBH</subfield>
    <subfield code="a">Stefan Menzel</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Honda Research Institute Europe GmBH</subfield>
    <subfield code="0">(orcid)0000-0002-1233-9584</subfield>
    <subfield code="a">Bernhard Sendhoff</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">University of Birmingham</subfield>
    <subfield code="a">Xin Yao</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">48323</subfield>
    <subfield code="z">md5:ffe8a44af11f26d00bf829fe37ebd3d2</subfield>
    <subfield code="u">https://zenodo.org/record/3859701/files/Output Data for WCCI 2020 ESR7 Gan Ruan.xlsx</subfield>
  </datafield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  </datafield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2020-05-27</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="p">openaire_data</subfield>
    <subfield code="p">user-ecole_itn</subfield>
    <subfield code="o">oai:zenodo.org:3859701</subfield>
  </datafield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="u">University of Birmingham</subfield>
    <subfield code="a">Gan Ruan</subfield>
  </datafield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">Computational Study on Effectiveness of Knowledge Transfer in Dynamic Multi-objective Optimization</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">user-ecole_itn</subfield>
  </datafield>
  <datafield tag="536" ind1=" " ind2=" ">
    <subfield code="c">766186</subfield>
    <subfield code="a">Experience-based Computation: Learning to Optimise</subfield>
  </datafield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="u">https://creativecommons.org/licenses/by-sa/4.0/legalcode</subfield>
    <subfield code="a">Creative Commons Attribution Share Alike 4.0 International</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2">opendefinition.org</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">&lt;p&gt;This file is the output data obtained when running the experiments from the paper below:&lt;/p&gt;

&lt;p&gt;Ruan, G., Minku, L., Menzel, S., Sendhoff, B., Yao., &amp;ldquo;Computational Study on Effectiveness of Knowledge Transfer in Dynamic Multi-objective Optimization&amp;rdquo;&amp;nbsp;&lt;em&gt;2020 IEEE Congress on Evolutionary Computation&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;Transfer learning has been used for solving multiple optimization and dynamic multi-objective optimization problems, since transfer learning is believed to be able to transfer useful information from one problem instance to help solving another related problem instance. This paper aims to study how effective transfer learning is in dynamic multi-objective optimization (DMO). Through computation time analysis of transfer learning, we show that the &amp;lsquo;inner&amp;rsquo; optimization problem introduced by transfer learning is very time-consuming. In order to enhance the efficiency, two alternatives are computationally investigated on a number of dynamic bi- and tri-objective test problems. Experimental results have shown that the greatly enhanced efficiency does not result in much degeneration on the performance of transfer learning. Considering the high computational cost of transfer learning, it is likely that the original purpose of using transfer learning in DMO might be negated. In other words, the computation time saved in optimization is eaten up by computationally expensive transfer learning. As a result, there is less gain than expected in the overall computational efficiency. To verify this, experiments have been conducted, regarding using computational cost of transfer learning to optimize randomly generated solutions. The results have demonstrated that the convergence and diversity of final solutions generated from the random solutions are significantly better than those generated from transferred solutions under the same total computational budget.&lt;/p&gt;</subfield>
  </datafield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="n">doi</subfield>
    <subfield code="i">isVersionOf</subfield>
    <subfield code="a">10.5281/zenodo.3859700</subfield>
  </datafield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.5281/zenodo.3859701</subfield>
    <subfield code="2">doi</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">dataset</subfield>
  </datafield>
</record>
26
3
views
downloads
All versions This version
Views 2626
Downloads 33
Data volume 145.0 kB145.0 kB
Unique views 2424
Unique downloads 33

Share

Cite as