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A Bayesian subset simulation approach to constrained global optimization of expensive-to-evaluate black-box functions

Abstract

This talk addresses the problem of derivative-free global optimization of a real-valued function under multiple inequality constraints. Both the objective function and the constraint functions are assumed to be smooth, nonlinear, expensive-to-evaluate black-box functions. As a consequence, the number of evaluations that can be used to carry out the optimization is very limited. We focus in this work on the case of strongly constrained problems, where finding a feasible design, using such a limited budget of simulations, is a challenge in itself. The method that we propose to overcome this difficulty has its roots in the recent literature on Gaussian process-based methods for reliability analysis—in particular, the Bayesian Subset Simulation (BSS) algorithm of Li, Bect and Vazquez—and multi-objective optimization. More specifically, we consider a decreasing sequence of nested subsets of the design space, which is defined and explored sequentially using a combination of Sequential Monte Carlo (SMC) techniques and sequential Bayesian design of experiments. The proposed method obtains promising result on challenging test cases from the literature.
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Dates and versions

hal-01078397 , version 1 (28-10-2014)
hal-01078397 , version 2 (30-10-2014)

Identifiers

  • HAL Id : hal-01078397 , version 2

Cite

Paul Feliot, Julien Bect, Emmanuel Vazquez. A Bayesian subset simulation approach to constrained global optimization of expensive-to-evaluate black-box functions. PGMO-COPI’14, Oct 2014, Palaiseau, France. ⟨hal-01078397v2⟩
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