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

Ingrida Steponavičė (), Mojdeh Shirazi-Manesh (), Rob Hyndman, Kate Smith-Miles () and Laura Villanova ()
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Ingrida Steponavičė: Monash University
Mojdeh Shirazi-Manesh: Monash University
Kate Smith-Miles: Monash University
Laura Villanova: Monash University

A chapter in Advances in Stochastic and Deterministic Global Optimization, 2016, pp 273-296 from Springer

Abstract: 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.

Keywords: Design of experiment; Space-filling; Low-discrepancy; Efficient global optimization (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/978-3-319-29975-4_15

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