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Application of Metamodel-Assisted Multiple-Gradient Descent Algorithm (MGDA) to Air-Cooling Duct Shape Optimization

Adrien Zerbinati 1 Jean-Antoine Désidéri 1 Régis Duvigneau 1 
1 OPALE - Optimization and control, numerical algorithms and integration of complex multidiscipline systems governed by PDE
CRISAM - Inria Sophia Antipolis - Méditerranée , JAD - Laboratoire Jean Alexandre Dieudonné : UMR6621
Abstract : MGDA stands for Multiple-Gradient Descent Algorithm, which was introduced in a previous report. MGDA was tested on several analytical test cases and also compared with a well-known Evolution Strategy algorithm, Pareto Archived Evolution Strategy (PAES). Using MGDA in a multi-objective optimization problem requires the evaluation of a substantial number of points with regard to criteria, and their gradients. In industrial test cases, in which computing the objective functions is CPU demanding, a variant of the method was to be found. Here, a metamodel-assisted MGDA is proposed and tested. The MGDA is assisted by a Kriging surrogate model construction. A first database is computed as an Latin Hypercube Sampling (LHS) distribution in the admissible design space, which is problem-dependent. Then, MGDA leads each database point to a non dominated set of the surrogate model. In this way, each function computation is made on the surrogate model at a negligible computational cost.
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Submitted on : Wednesday, October 17, 2012 - 4:52:11 PM
Last modification on : Thursday, August 4, 2022 - 4:52:47 PM
Long-term archiving on: : Saturday, December 17, 2016 - 2:11:00 AM


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  • HAL Id : hal-00742948, version 1



Adrien Zerbinati, Jean-Antoine Désidéri, Régis Duvigneau. Application of Metamodel-Assisted Multiple-Gradient Descent Algorithm (MGDA) to Air-Cooling Duct Shape Optimization. ECCOMAS - European Congress on Computational Methods in Applied Sciences and Engineering - 2012, Sep 2012, Vienna, Austria. ⟨hal-00742948⟩



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