Both the Nelder–Mead and EGO solvers perform various internal operations that involve evaluating the objective function for multiple sets of control variables. In the Nelder–Mead method, this occurs as part of the simplex adjustments, while in EGO, the objective function is evaluated for initial sets of sampled control variables and potentially for multiple new sets of control variables selected simultaneously from the acquisition function. In both methods, these evaluations are independent of each other, allowing them to be computed in parallel. You can enable this distributed approach in the optimization study step using the Distribute parametric sweep checkbox.

Similarly, you might be using a Parametric Sweep to solve several independent optimization problems in order to generate a Pareto optimal curve. You can use the same Distribute parametric sweep checkbox to ensure that the optimizations are carried out in parallel. Note that the optimizations have to be independent, and therefore you cannot combine this with the Reuse solution from previous step checkbox.