Parallel Hierarchical Evolutionary Algorithms for Multicriteria Design Optimization Problems in Aerospace Engineering

New parallel hierarchical multi -objective optimization approaches based on Genetic Algorithms (GAs) with Nash scenarios of Game Theory are i nvestigated for solving high lift multi -element airfoil design problems in Aerodynamics on distributed parallel enviro nments. High lift devices from a multi -element airfoil system evolving in a complex viscous flow at low Mach number conditions are still critical challenges for modern aircraft design. A multi -objective optimization methodology presented here relies on b inary coded Genetic Algorithms (GAs) and coupled with Game Strategies (GS). The design variables of such optimization problems are split between se veral players, the global multi -criterion optimization pro blem being replaced by several sub optimizations o perating in the decomposed search space. The viscous flow around the multi -element system is sim ulated with a non-expensive but non-differentiable CFD solver coupling a panel approach with the vi scous effects modeled by laminar or turbulent boun dary layers and wakes. Two problems solved in this paper, for shape/position optimization, are: a) Inverse problem which consist in retrieving a target pressure distrib ution, b) Lift optimization prob lem which consist in maximizing the lift of a multi -element airfoil. N umerical results show that hierarchical GAs combined with Nash strategy are more efficient and robust than simple GAs, not only for reconstruction pro blems, but also for multidisciplinar y optimiz ation problems in aerodynamic design