Optimization of systems is often plagued by computationally expensive simulations and the need for iterative analysis to resolve coupling among subsystems. These challenges are typically severe enough as to prohibit formal system optimization. This paper formulates two new methods to parallelize the optimization of multidisciplinary systems. The first method decomposes the system optimization problem into multiple subsystem optimizations that are solved in parallel. The second method generates a list of designs at which computationally expensive simulations should be run, evaluates those designs in parallel, and then solves an inexpensive surrogate-based optimization problem. Both methods enable the use of multifidelity optimization to find an optimal solution with respect to the highest-fidelity models available. In addition, both methods exploit high-fidelity sensitivity information if available, but do not require gradients of the high-fidelity models. Thus, the methods are applicable to problems with black-box codes and/or noisy function evaluations. The two methods are demonstrated on three analytical optimization problems, and a multidisciplinary, multifidelity aerostructural design optimization problem.
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