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

Authors

Ingrida Steponavičė, Mojdeh Shirazi‑Manesh, Rob J Hyndman, Kate Smith‑Miles, Laura Villanova

Published

28 February 2016

Publication details

In Advances in Stochastic and Deterministic Global Optimization, ed. P.M. Pardalos, A. Zhigljavsky, J. Žilinskas. Springer, pp. 273–296

Links

 

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.