DagSemProc.09041.7.pdf
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A Polynomial Chaos Approach to Robust Multiobjective Optimization
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Dagstuhl Seminar Proceedings, Volume 9041
Series: Dagstuhl Seminar Proceedings (DagSemProc) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2009-05-11
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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
https://doi.org/10.4230/DagSemProc.09041.7
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.
Keywords
- Uncertainty Quantification
- Multiobjective Robust Design
- Monte Carlo
- Latin Hypercube
- Polynomial Chaos
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