When a point suggested by NMS is outside the bounds of the decision variables, we remove the dimension that is violating a constraint and carry out NMS in the reduced space. The optimal point from this reduced space is applied to the full problem. Second, if the optimisation problem includes linear constraints, we penalize new vertices suggested by NMS that violate at least one constraint. The geometric formation hyperparameters of the NMS we use are similar to the scipy package: ρ = 1 (refletion), χ = 2 (expansion), ψ = 0.5 (contraction), σ = 0.5 (shrink).