After clustering of the results, between one and two unique minima are identified for TP1 through TP4. Relatively strict criteria are used when clustering the minima: both the objective function value as well as the scaled parameter values must be in agreement between two minima for them to be clustered together. In TP2 and TP4, two distinct minima are reported. In both test problems, the minima correspond to fairly similar objective function values, with a relative difference of only 0.7% and 0.1% respectively. In contrast, the scaled parameter values between the minima differ by as much as 71.9% and 56.7% for TP2 and TP4 respectively. The reason why the parameter values between minima are able to differ so much without significantly influencing the resulting objective function values might stem from the choice to simultaneously optimise A and C for every pairwise interaction. There is inherently some correlation between these two parameters: greater repulsions due to a larger A value can be counterbalanced by greater attractions from a larger C value (and vice versa if their values are decreased). This seems to be reflected in the two of minima of these test problems, wherein one minimum contains a set of larger A and C values while the other contains the contrasting pair of smaller A and C values. The correlation between A and C is not exact, hence the inclusion of more data could aid in breaking the close degeneracy in these solutions. Ultimately, the discovery of multiple minima on the landscape highlights the need for global optimisation techniques such as Sobol' sampling to thoroughly explore the parameter space. The high convergence rate and the small number of minima identified suggest that the best solution has been found for each of these test problems within the sampling range of the Sobol' sequence.