A Multifidelity Gradient-Free Optimization Method and Application to Aerodynamic Design
Dev Rajnarayan, Alex Haas, Ilan M. Kroo · 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference · 2008
The use of expensive simulations in engineering design optimization often rules out conventional techniques for design optimization for a variety of reasons, such as lack of smoothness, unavailability of gradient information, presence of multiple local optima, and most importantly, limits on available computing resources and time. Often, the designer also has access to lower-fidelity simulations that may suffer from poor accuracy in some regions of the design space, but are much cheaper to evaluate than the original expensive simulation. We can accelerate the design process by efficiently managing these models of various fidelities. There has been previous research in this area: some algorithms in the literature first estimate of the relationships between these models, and then perform optimization on the corrected low-fidelity models. Others adaptively select new high-fidelity designs, but these usually require gradient information; those that relax this requirement use a trust-region-based local search method. In contrast, most global optimization methods in the literature require smoothness, and do not incorporate multifidelity analyses. We would like to combine the advantages of all these techniques, and in this paper, we describe a method to incorporate models of two fidelities and perform a gradient-free