Multiple metamodels for robustness estimation in multi-objective robust optimization

Pramudita Satria Palar, Koji Shimoyama

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Due to the excessive cost of Monte Carlo simulation, metamodel is now frequently used to accelerate the process of robustness estimation. In this paper, we explore the use of multiple metamodels for robustness evaluation in multi-objective evolutionary robust optimization under parametric uncertainty. The concept is to build several different metamodel types, and employ cross-validation to pick the best metamodel or to create an ensemble of metamodels. Three types of metamodel were investigated: sparse polynomial chaos expansion (PCE), Kriging, and 2nd order polynomial regression (PR). Numerical study on robust optimization of two test problems was performed. The result shows that the ensemble approach works well when all the constituent metamodel is sufficiently accurate, while the best scheme is more favored when there is a constituent metamodel with poor quality. Moreover, besides the accuracy, we found that it is also important to preserve the trend and smoothness of the decision variables-robustness relationship. PR, which is the less accurate metamodel from all, can found a better representation of the Pareto front than the sparse PCE.

Original languageEnglish
Title of host publicationEvolutionary Multi-Criterion Optimization - 9th International Conference, EMO 2017, Proceedings
EditorsOliver Schütze, Gunter Rudolph, Kathrin Klamroth, Yaochu Jin, Heike Trautmann, Christian Grimme, Margaret Wiecek
PublisherSpringer-Verlag
Pages469-483
Number of pages15
ISBN (Print)9783319541563
DOIs
Publication statusPublished - 2017 Jan 1
Event9th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2017 - Munster, Germany
Duration: 2017 Mar 192017 Mar 22

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10173 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2017
CountryGermany
CityMunster
Period17/3/1917/3/22

Keywords

  • Evolutionary algorithm
  • Multiobjective robust optimization
  • Multiple metamodels
  • Robustness estimation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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