Evolutionary algorithms are powerful optimizers often used to explore the trade-off between performance and robustness in robust optimization. A popular methodology for un- certainty quantiffication (UQ) in evaluating robustness is through use of a polynomial chaos (PC) expansion. To make best use of the available computational budget, improvements in the performance of both optimizer and UQ method are desired. In this paper we present an approach for aerodynamic robust optimization which consists of a decomposition-based optimizer (MOEA/D) and multi-ffidelity point collocation non-intrusive PC. The inherited diversity that a decomposition-based optimizer produces is a benefficial trait for multi-objective robust optimization applications. In our UQ approach, the availability of the multi-ffidelity simulations is incorporated within the point collocation PC to allow exible numbers of samples and calculate the effiect of uncertainty efficiently. A comparison between MOEA/D and NSGA-II is performed for a subsonic application. This shows that the decomposition-based optimizer is able to find a more diverse Pareto front. The multi-ffidelity robust optimization framework is then demonstrated on a transonic airfoil robust optimization application with the goals of maximizing lift-to-drag ratio (L/D) while minimizing the sensitivity to aleatory uncertainties. The multi-ffidelity point collocation non-intrusive PC approach is able reduce the computational time needed for UQ. In the transonic case, the solution with the maximum mean of L/D is preferred over the other airfoils even though it is accompanied by a high standard deviation in L/D. The stochastic response surface shows that its minimum value of L/D over the response surface is still higher than that of the airfoil with the minimum standard deviation. This shows that it is important to examine the trend of the stochastic response surface before conclusions are drawn and decisions made
