Enhanced Collaborative Optimization: Application to an Analytic Test Problem and Aircraft Design
Brian D. Roth, Ilan M. Kroo · 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference · 2008
This paper provides an introduction to a new method for distributed optimization based on collaborative optimization, a decomposition-based method for the optimization of complex multidisciplinary designs. The key idea in this approach is to include models of the global objective and all of the subspace constraints in each subspace optimization problem while maintaining the low dimensionality of the system level (coordination) problem. Results from an analytic test case and an aircraft family design problem suggest that the new approach is robust and leads to a substantial reduction in computational eort. Collaborative optimization (CO) is a method for the design of complex, multidisciplinary systems that was originally proposed 1 in 1994. CO is one of several decomposition-based methods that divides a design problem along disciplinary (or other convenient) boundaries. The idea is to mirror the natural divisions found in aerospace design companies. In these settings, engineers are often divided into design groups by disciplinary expertise. Disciplinary analysis tools tend to be complex in nature, and it is often impractical to integrate multiple analysis codes for the purpose of multidisciplinary optimization. Rather, CO oers a means of coordinating separate analyses, even leveraging discipline-specific optimization techniques. Relative to other decomposition-based methods, CO provides the disciplinary subspaces with an unusually high level of autonomy. This enhances their ability to independently make design decisions pertinent primarily to their discipline.