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On Sampling Methods for Costly Multi-Objective Black-Box Optimization

In: Advances in Stochastic and Deterministic Global Optimization

Author

Listed:
  • Ingrida Steponavičė

    (Monash University)

  • Mojdeh Shirazi-Manesh

    (Monash University)

  • Rob J. Hyndman

    (Monash University)

  • Kate Smith-Miles

    (Monash University)

  • Laura Villanova

    (Monash University)

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 impact 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.

Suggested Citation

  • Ingrida Steponavičė & Mojdeh Shirazi-Manesh & Rob J. Hyndman & Kate Smith-Miles & Laura Villanova, 2016. "On Sampling Methods for Costly Multi-Objective Black-Box Optimization," Springer Optimization and Its Applications, in: Panos M. Pardalos & Anatoly Zhigljavsky & Julius Žilinskas (ed.), Advances in Stochastic and Deterministic Global Optimization, pages 273-296, Springer.
    • Handle: RePEc:spr:spochp:978-3-319-29975-4_15
    DOI: 10.1007/978-3-319-29975-4_15
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    Cited by:

    1. A. Candelieri & R. Perego & F. Archetti, 2018. "Bayesian optimization of pump operations in water distribution systems," Journal of Global Optimization, Springer, vol. 71(1), pages 213-235, May.

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