The Nelder-Mead Simplex (NMS) is a local optimisation algorithm. NMS optimises an objective function using multi-dimensional polyhedra. For an optimisation problem with n DoF degrees of freedom, the NMS spans a polyhedra with n DoF +1 vertices within the feasible domain. Each vertex represents a set of conditions with the corresponding objective function value. During the optimisation process, vertices Table : Capabilities of optimisation strategies: modified Nelder-Mead Simplex (NMS), SNOB-FIT, Bayesian optimisation (BO), and Reinforcement Learning (RL). We implement the NMS similar to the python SciPy package with two modifications suggested by by Cortes-Borda et al. First, we include a modification to account for bounded decision variables.