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Communication dans un congrès

Metamodel Assisted Evolutionary Algorithm for Multi-objective Optimization of Non-steady Metal Forming Problems

Abstract : Multiobjective optimization problems are considered in the field of nonsteady metal forming processes, such as forging or wire drawing. The Pareto optimal front of the problem solution set is calculated by a Genetic Algorithm. In order to reduce the inherent computational cost of such algorithms, a surrogate model is developed and replaces the exact the function simulations. It is based on the Meshless Finite Difference Method and is coupled to the NSGAII Evolutionary Multiobjective Optimization Algorithm, in a way that uses the merit function. This function offers the best way to select new evaluation points: it combines the exploitation of obtained results with the exploration of parameter space. The algorithm is evaluated on a wide range of analytical multiobjective optimization problems, showing the importance to update the metamodel along with the algorithm convergence. The application to metal forming multiobjective optimization problems show both the efficiency of the metamodel based algorithms and the type of practical information that can be derived from a multiobjective approach.
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Communication dans un congrès
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https://hal-mines-paristech.archives-ouvertes.fr/hal-00851431
Contributeur : Magalie Prudon <>
Soumis le : mercredi 14 août 2013 - 10:49:35
Dernière modification le : mercredi 23 septembre 2020 - 04:23:38
Archivage à long terme le : : vendredi 15 novembre 2013 - 09:27:20

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MS01-017.pdf
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Mohsen Ejday, Lionel Fourment. Metamodel Assisted Evolutionary Algorithm for Multi-objective Optimization of Non-steady Metal Forming Problems. 13th ESAFORM Conference on Material Forming, Apr 2010, Brescia, Italy. pp.Pages 5-8, ⟨10.1007/s12289-010-0689-0⟩. ⟨hal-00851431⟩

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