Skip to Main content Skip to Navigation
Journal articles

Kriging surrogates for evolutionary multi-objective optimization of CPU intensive sheet metal forming applications

Abstract : The aim of this paper is to present a method to perform evolutionary multi-objective optimization of CPU intensive sheet metal forming applications using kriging surrogates. Two main ingredients are employed to achieve this goal. First of all, given a learning dataset, the kriging surrogate is designed to minimize the leave-one-out error. Secondly, during the optimization, new data points are added to the learning set to update the surrogate locally (by well chosen points on the current Pareto front) and globally (by maximum kriging variance points over the entire design landscape). The ability of the method to capture Pareto fronts with accuracy is demonstrated on the well-known ZDT test functions. The method is then tested on a real-life problem, the simultaneous minimization of springback and failure for a three-dimensional CPU intensive high strength steel stamping industrial use case.
Complete list of metadatas

https://hal.inria.fr/hal-01109996
Contributor : Abderrahmane Habbal <>
Submitted on : Tuesday, January 27, 2015 - 12:00:35 PM
Last modification on : Friday, July 17, 2020 - 2:57:08 PM

Identifiers

Citation

Mohamed Hamdaoui, Fatima-Zahra Oujebbour, Abderrahmane Habbal, Piotr Breitkopf, Pierre Villon. Kriging surrogates for evolutionary multi-objective optimization of CPU intensive sheet metal forming applications. International Journal of Material Forming, Springer Verlag, 2014, 1960-6206 (3), pp.12. ⟨10.1007/s12289-014-1190-y⟩. ⟨hal-01109996⟩

Share

Metrics

Record views

643