A Multiobjective Adaptive Surrogate Modeling-Based Optimization (MO-ASMO) Framework Using Efficient Sampling Strategies (bibtex)
by Yong Hoon Lee, Rebecca E Corman, Randy H Ewoldt, James T Allison
Reference:
Yong Hoon Lee, Rebecca E Corman, Randy H Ewoldt, James T Allison. 'A Multiobjective Adaptive Surrogate Modeling-Based Optimization (MO-ASMO) Framework Using Efficient Sampling Strategies.' In ASME 2017 International Design Engineering Technical Conferences, DETC2017-67541, p. V02BT03A023, Cleveland, OH, USA, Aug 2017.
Bibtex Entry:
@Inproceedings{Lee2017b,
  Title                    = {A Multiobjective Adaptive Surrogate Modeling-Based Optimization (MO-ASMO) Framework Using Efficient Sampling Strategies},
  Address                  = {Cleveland, OH, USA},
  Author                   = {Lee, Yong Hoon and Corman, Rebecca E and Ewoldt, Randy H and Allison, James T},
  Booktitle                = {ASME 2017 International Design Engineering Technical Conferences},
  Month                    = aug,
  Year                     = {2017},
  Number                   = {DETC2017-67541},
  Pages                    = {V02BT03A023},
  Doi                      = {10.1115/DETC2017-67541},
  Pdf                      = {http://systemdesign.illinois.edu/publications/Lee2017b.pdf},
  ESDLnote                 = {<img src="./images/custom_icons/code_icon.svg" style="width: auto; height: 18px;"> (<a href="https://github.com/yonghoonlee/MO-ASMO-I">Main MATLAB Code</a>, <a href="https://github.com/yonghoonlee/MO_TestFunction_Lee2017a">Test Function Code</a>), <i class="fi-video size-18"></i> (<a href="https://youtu.be/mDmI1cHgXmE">Video</a>)},
  Esdlid                   = {C52},
}
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