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

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

doi:10.1007/978-3-319-29975-4_15

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 proceeding › Chapter (Book) › Research › peer-review

21 Citations (Scopus)

Abstract

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 language	English
Title of host publication	Advances in Stochastic and Deterministic Global Optimization
Editors	Panos M. Pardalos, Anatoly Zhigljavsky, Julius Žilinskas
Place of Publication	Switzerland
Publisher	Springer
Pages	273-296
Number of pages	24
Volume	107
ISBN (Electronic)	9783319299754
ISBN (Print)	9783319299730
DOIs	https://doi.org/10.1007/978-3-319-29975-4_15
Publication status	Published - 5 Nov 2016

Publication series

Name	Springer Optimization and Its Applications
Publisher	Springer International Publishing
Number	Part III
Volume	107

Keywords

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

Access to Document

10.1007/978-3-319-29975-4_15

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

Steponavice, Ingrida ; Shirazi Manesh, Mojdeh ; Hyndman, Rob et al. / On sampling methods for costly multi-objective black-box optimization. Advances in Stochastic and Deterministic Global Optimization. editor / Panos M. Pardalos ; Anatoly Zhigljavsky ; Julius Žilinskas. Vol. 107 Switzerland : Springer, 2016. pp. 273-296 (Springer Optimization and Its Applications; Part III).

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abstract = "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.",

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

On sampling methods for costly multi-objective black-box optimization. / Steponavice, Ingrida; Shirazi Manesh, Mojdeh; Hyndman, Rob et al.
Advances in Stochastic and Deterministic Global Optimization. ed. / Panos M. Pardalos; Anatoly Zhigljavsky; Julius Žilinskas. Vol. 107 Switzerland: Springer, 2016. p. 273-296 (Springer Optimization and Its Applications; Vol. 107, No. Part III).

Research output: Chapter in Book/Report/Conference proceeding › Chapter (Book) › Research › peer-review

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AU - Villanova, Laura

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AB - 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.

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KW - Space-filling

KW - Low-discrepancy

KW - Efficient global optimization

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Steponavice I, Shirazi Manesh M, Hyndman R, Smith-Miles K, Villanova L. On sampling methods for costly multi-objective black-box optimization. In Pardalos PM, Zhigljavsky A, Žilinskas J, editors, Advances in Stochastic and Deterministic Global Optimization. Vol. 107. Switzerland: Springer. 2016. p. 273-296. (Springer Optimization and Its Applications; Part III). doi: 10.1007/978-3-319-29975-4_15

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

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