On sampling methods for costly multi-objective black-box optimization

Ingrida Steponavice, Mojdeh Shirazi Manesh, Rob Hyndman, Kate Smith-Miles, Laura Villanova

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review

7 Citations (Scopus)


We investigate the impact of different sampling techniques on the performance of multi-objective optimization methods applied to costly black-box optimization problems. Such problems are often solved using an algorithm in which a surrogate model approximates the true objective function and provides predicted objective values at a lower cost. As the surrogate model is based on evaluations of a small number of points, the quality of the initial sample can have a great effect on the overall effectiveness of the optimization. In this study, we demonstrate how various sampling techniques affect the results of applying different optimization algorithms to a set of benchmark problems. Additionally, some recommendations on usage of sampling methods are provided.
Original languageEnglish
Title of host publicationAdvances in Stochastic and Deterministic Global Optimization
EditorsPanos M. Pardalos, Anatoly Zhigljavsky, Julius Žilinskas
Place of PublicationSwitzerland
Number of pages24
ISBN (Electronic)9783319299754
ISBN (Print)9783319299730
Publication statusPublished - 5 Nov 2016

Publication series

NameSpringer Optimization and Its Applications
PublisherSpringer International Publishing
NumberPart III


  • Design of experiment
  • Space-filling
  • Low-discrepancy
  • Efficient global optimization

Cite this

Steponavice, I., Shirazi Manesh, M., Hyndman, R., Smith-Miles, K., & Villanova, L. (2016). On sampling methods for costly multi-objective black-box optimization. In P. M. Pardalos, A. Zhigljavsky, & J. Žilinskas (Eds.), Advances in Stochastic and Deterministic Global Optimization (Vol. 107, pp. 273-296). (Springer Optimization and Its Applications; Vol. 107, No. Part III). Springer. https://doi.org/10.1007/978-3-319-29975-4_15