ENBIS-18 in Nancy

2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018

Kriging-Based Robust Optimization

3 September 2018, 14:00 – 14:20

Abstract

Submitted by
Celine Helbert
Authors
Celine Helbert (Ecole Centrale de Lyon), Melina Ribaud (Ecole Centrale de Lyon), Christophette Blanchet (Ecole Centrale de Lyon), Frederic Gillot (Ecole Centrale de Lyon)
Abstract
In this paper we expose our benchmarking work for kriging-assisted robust optimization for limited budgets problems. In industrial context production fluctuations can often impact the optimal design. Our robustness criterion is then naturally defined as the variance of the quantity of interest in the neighborhood of a design solution. First we introduce two kriging-based approximations of this robustness criterion and show that the second one based on the observation of first and second derivatives is much more efficient. Then we propose to address the robust optimization issue by solving a multiobjective optimization problem on the quantity of interest and the robustness criterion. We detail 10 different relevant strategies and compare them for the same budget of 54 evaluated points on the 2D Six-Hump Camel function as objective function. All strategies are based on a NSGA II algorithm and 9 starting points. Sets of 9 new points are then added after each iteration of the optimization algorithm to increase the kriging accuracy of the objective function and its robustness criterion. Results tend to show that a balance between computational effort and accuracy for added point is achieved when using Kriging Believer or Constant Liar strategy for the function as well as the robustness criterion.
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