The DARPA initiated Quiet Supersonic Platform (QSP) program 
is an excellent example problem requiring multidisciplinary 
design optimization (MDO) in which the noise level of the 
ground boom signature of a supersonic business jet should 
be significantly reduced while challenging aerodynamic 
performance requirements must be met at the same time. To 
address this kind of problem, an efficient and robust design 
methodology using approximation techniques such as response 
surface and Kriging methods, augmented by gradient information, 
has been developed and tested on simple analytic functions as 
well as on more realistic design test cases. An integrated 
boom prediction tool incorporating fully nonlinear CFD 
analyses has been developed to provide required sample data 
for the QSP problem. A multiobjective optimization to 
simultaneously minimize boom and drag at fixed lift has been 
performed using a genetic algorithm based on different kinds 
of approximation models to search for the Pareto design front. 
The results show that the proposed design procedure achieves 
its robustness and efficiency by using well-behaved low-fidelity 
approximations to more computationally expensive CFD analyses 
and by enhancing them with the gradient information available. 
In addition, various ways to integrate the information acquired 
from the approximation models into a genetic algorithm search 
method have been demonstrated to reduce its large computational 
costs and to make its use feasible for realistic high-dimensional 
design problems.