Many problems do not possess the required structure to be addressed by traditional optimization methods. In this presentation, we focus on situations where the evaluation of the functions to be minimized and those delimiting the feasible region are assessed by running a computer simulation, expensive in terms of computational time. These functions are usually nonsmooth, discontinuous, and may even be contaminated by numerical noise. Such problems are referred to as «Blackbox optimization problems». We will present a brief history of direct-search methods designed for these problems, culminating to the MADS algorithm with our Nomad implementation. We will also describe some of the industrial problems on which we have worked.