Multi-objective multi-fidelity optimization with ordinal transformation and optimal sampling

Haobin Li, Yueqi Li, Loo Hay Lee, Ek Peng Chew, Giulia Pedrielli, Chun Hung Chen

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

11 Scopus citations


In simulation-optimization, the accurate evaluation of candidate solutions can be obtained by running a high-fidelity model, which is fully featured but time-consuming. Less expensive and lower fidelity models can be particularly useful in simulation-optimization settings. However, the procedure has to account for the inaccuracy of the low fidelity model. Xu et al. (2015) proposed the MO2TOS, a Multi-fidelity Optimization (MO) algorithm, which introduces the concept of ordinal transformation (OT) and uses optimal sampling (OS) to exploit models of multiple fidelities for efficient optimization. In this paper, we propose MO-MO2TOS for the multi-objective case using the concepts of non-dominated sorting and crowding distance to perform OT and OS in this setting. Numerical experiments show the satisfactory performance of the procedure while analyzing the behavior of MO-MO2TOS under different consistency scenarios of the low-fidelity model. This analysis provides insights on future studies in this area.

Original languageEnglish (US)
Title of host publication2015 Winter Simulation Conference, WSC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages12
ISBN (Electronic)9781467397438
StatePublished - Feb 16 2016
Externally publishedYes
EventWinter Simulation Conference, WSC 2015 - Huntington Beach, United States
Duration: Dec 6 2015Dec 9 2015

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


OtherWinter Simulation Conference, WSC 2015
Country/TerritoryUnited States
CityHuntington Beach

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications


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