For this single-objective optimisation test function, the diversity of the swarm and the continuous sampling of the parameter space of the CPSO come into play. The search converges to the best solution with high statistical confidence, except for the high-dimensional parameter spaces, where the swarm tends to get caught in a local minimum. For the latter, the performance can be improved by enlarging the swarm size or increasing the competitivity parameter γ (see SI -Table .9 and S. Applying a fixed step length (δ = 0.1, δ = 0.05, and δ = 0.01) during the SOPPI optimisation runs achieved the worst results of all tested algorithms. However, assuming an initial step length of δ = 0.1 and a reduction of 1 % every 10 iterations, the variable SOPPI approach was able to keep up with the performance of CPSO and even outperform it in the 32-dimensional parameter space (see SI -Table .23).