A Polynomial Chaos Approach to Robust Multiobjective Optimization

Poles, Silvia; Lovison, Alberto

doi:10.4230/DagSemProc.09041.7

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A Polynomial Chaos Approach to Robust Multiobjective Optimization

Authors Silvia Poles, Alberto Lovison

Part of: Volume: Dagstuhl Seminar Proceedings, Volume 9041
Part of: Series: Dagstuhl Seminar Proceedings (DagSemProc)
License: Creative Commons Attribution 4.0 International license
Publication Date: 2009-05-11

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DagSemProc.09041.7.pdf

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Document Identifiers

DOI: 10.4230/DagSemProc.09041.7
URN: urn:nbn:de:0030-drops-20009

Subject Classification

Keywords

Uncertainty Quantification
Multiobjective Robust Design
Monte Carlo
Latin Hypercube
Polynomial Chaos

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Abstract

Robust design optimization is a modeling methodology, combined with a suite of computational tools, which is aimed to solve problems where some kind of uncertainty occurs in the data or in the model.
This paper explores robust optimization complexity in the multiobjective case, describing a new approach by means of Polynomial Chaos expansions (PCE). The aim of this paper is to demonstrate that the use of PCE may help and speed up the optimization process if compared to standard approaches such as Monte Carlo and Latin Hypercube sampling.

Cite As Get BibTex

Silvia Poles and Alberto Lovison. A Polynomial Chaos Approach to Robust Multiobjective Optimization. In Hybrid and Robust Approaches to Multiobjective Optimization. Dagstuhl Seminar Proceedings, Volume 9041, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009) https://doi.org/10.4230/DagSemProc.09041.7

Author Details

Silvia Poles

Alberto Lovison

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