Derivative-free optimization algorithms are needed to solve real-world engineering problems that have computationally expensive and noisy objective function and constraint evaluations. In particular, we are focused on problems that involve running cumbersome simulation codes with run times measured in hours. In such cases, attempts to compute derivatives can prove futile because analytical derivatives are typically unavailable and noise limits the accuracy of numerical approximations. Furthermore, the objective and constraint functions may be inherently nonsmooth, i.e., because the underlying model is nonsmooth.