Meta-model-assisted MGDA for multi-objective functional optimization - Archive ouverte HAL
Article Dans Une Revue Computers and Fluids Année : 2014

Meta-model-assisted MGDA for multi-objective functional optimization

Résumé

A novel numerical method for multi-objective differentiable optimization, the Multiple-Gradient Descent Algorithmm (MGDA), has been proposed in [8] [11] to identify Pareto fronts. In MGDA, a direction of search for which the directional gradients of the objective functions are all negative, and often equal by construction [12], is identified and used in a steepest-descent-type iteration. The method converges to Pareto-optimal points. MGDA is here briefly reviewed to outline its principal theoretical properties and applied first to a classical mathematical test-case for illustration. The method is then ex-tended encompass cases where the functional gradients are approximated via meta-models, as it is often the case in complex situations, and demonstrated on three optimum-shape design problems in compressible aerodynamics.The first problem is purely related to aerodynamic performance. It is a wing shape optimization exercise w.r.t. lift and drag in typical transonic cruise
Fichier principal
Vignette du fichier
DAAP14008.1391007404.pdf (2.69 Mo) Télécharger le fichier
Origine Fichiers éditeurs autorisés sur une archive ouverte
Loading...

Dates et versions

hal-01082595 , version 1 (13-11-2014)

Identifiants

Citer

Adrien Zerbinati, Andrea Minelli, Imane Ghazlane, Jean-Antoine Desideri. Meta-model-assisted MGDA for multi-objective functional optimization. Computers and Fluids, 2014, 102 (10), pp.116 - 130. ⟨10.1016/j.compfluid.2014.06.018⟩. ⟨hal-01082595⟩
247 Consultations
265 Téléchargements

Altmetric

Partager

More