Using Gaussian Processes for Metamodeling in Robust Optimization Problems

Autores/as

  • Claudemir Mota da Cruz State University of Santa Cruz - DCEX, Ilhéus, BA, Brazil; Polytechnic Institute, Rio de Janeiro State University - Nova Friburgo, RJ, Brazil https://orcid.org/0000-0001-7264-6220
  • Fran Sérgio Lobato Federal University of Uberlândia, Faculty of Chemical Engineering - Uberlândia, MG, Brazil https://orcid.org/0000-0002-7401-4718
  • Gustavo Barbosa Libotte Polytechnic Institute, Rio de Janeiro State University - Nova Friburgo, RJ, Brazil

DOI:

https://doi.org/10.18540/jcecvl9iss10pp17809

Palabras clave:

Gaussian. Process. Metamodels. Optimization. Robust

Resumen

This article proposes an approach based on Gaussian Processes for building metamodels for robust optimization problems that seek to reduce the computational effort required to quantify uncertainties. The approach is applied to two cases: a low-dimensional benchmark problem and a high-dimensional structural design, which consists of minimizing the mass of a structure formed by bars of different materials and diameters, subjected to point loads in different locations. The cases are modeled as robust optimization problems, where the objective function is estimated by a Gaussian Process and the optimization procedure uses a population meta-heuristic. The results indicate that the proposed approach is effective in reducing the number of objective function evaluations required to obtain a robust solution, with no significant statistical differences in the quality of solutions achieved.

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Publicado

2023-12-29

Cómo citar

Cruz, C. M. da, Lobato, F. S., & Libotte, G. B. (2023). Using Gaussian Processes for Metamodeling in Robust Optimization Problems. The Journal of Engineering and Exact Sciences, 9(10), 17809. https://doi.org/10.18540/jcecvl9iss10pp17809

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