We show how pattern search methods can be adapted to the optimization problem contexts where there are ways to provide points that can lead to an objective function decrease. The paradigm here is that it is the user and the optimization algorithm together, and not the optimization algorithm alone, that lead the calculation of new points. We are especially concerned with problems where objective function evaluations are expensive and for which parallel computing is available.