V-Shaped sampling based on kendall-distance to enhance optimization with ranks

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

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

2 Scopus citations


In the area of discrete optimization via simulation (DOvS), optimization over rank values has been of concern in computer science and, more recently, in multi-fidelity simulation optimization. Specifically, Chen et al. (2015) proposes the concept of Ordinal Transformation to translate multi-dimensional discrete optimization problems into single-dimensional problems which are simpler, and the transformed solution space is referred as ordinal space. In this paper, we build on the idea of ordinal transformation and its properties in order to derive an efficient sampling algorithm for identifying the solution with the best rank in the setting of multi-fidelity optimization. We refer to this algorithm as V-shaped and we use the concept of Kendall distance adopted in the machine learning theory, in order to characterize solutions in the OT space. The algorithm is presented for the first time and preliminary performance results are provided comparing the algorithm with the sampling proposed in Chen et al. (2015).

Original languageEnglish (US)
Title of host publication2016 Winter Simulation Conference
Subtitle of host publicationSimulating Complex Service Systems, WSC 2016
EditorsTheresa M. Roeder, Peter I. Frazier, Robert Szechtman, Enlu Zhou
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages11
ISBN (Electronic)9781509044863
StatePublished - Jul 2 2016
Externally publishedYes
Event2016 Winter Simulation Conference, WSC 2016 - Arlington, United States
Duration: Dec 11 2016Dec 14 2016

Publication series

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


Other2016 Winter Simulation Conference, WSC 2016
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications


Dive into the research topics of 'V-Shaped sampling based on kendall-distance to enhance optimization with ranks'. Together they form a unique fingerprint.

Cite this