  Robust optimization is a method for optimization under uncertainties in
engineering systems and designs for applications ranging from aeronautics to
nuclear. In a robust design process, parameter variability (or uncertainty) is
incorporated into the engineering systems' optimization process to assure the
systems' quality and reliability. This chapter focuses on a robust optimization
approach for developing robust and reliable advanced systems and explains the
framework for using uncertainty quantification and optimization techniques. For
the uncertainty analysis, a polynomial chaos-based approach is combined with
the optimization algorithms MOSA (Multi-Objective Simulated Annealing), and the
process is discussed with a simplified test function. For the optimization
process, gradient-free genetic algorithms are considered as the optimizer scans
the whole design space, and the optimal values are not always dependent on the
initial values.
