Algorithme À Gradients Multiples Pour l'Optimisation Multiobjectif
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A l'instar des algorithmes mono-objectif, les gradients des critères, ainsi que les dérivées successives, apportent des informations utiles sur la décroissance des fonctions. De plus, de nombreuses méthodes numériques permettent d'obtenir ces valeurs pour un coût modéré. En s'appuyant sur les résultats théoriques obtenus, nous proposons un algorithme basé sur l'utilisation des gradients de descente. Ces travaux résument la caractérisation théorique de cette méthode et la validation sur des cas test analytiques. Dans le cas où les gradients ne sont pas accessibles, nous proposons une stratégie basée sur la construction de métamodèles de type Krigeage. Ainsi, au cours de l'optimisation, les critères sont évalués sur une surface de réponse et non par simulation. Le temps de calcul est considérablement réduit, au détriment de la précision. La méthode est alors couplée à une stratégie de progression du métamodèle. Nous proposons d'utiliser cette méthode d'optimisation pour résoudre un problème classique en aéronautique de minimisation de trainée et de maximisation de portance. Cette étude est réalisée sur un profil d'aile simplifié d'avion d'affaire de type NACA0012.
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