For high-dimensional tasks, parallel optimisation can improve the accuracy, convergence, and overall performance of the optimisation process. The simplest approach of this sort is multi-start gradient descent. In this method, the initial points are distributed randomly with space-filling techniques and gradient descent performed independently for each starting point. During optimisation, the points will fall into their local minima automatically, without the need for explicit space partitioning. However, this approach is limited to situations where the optimisation function is known and is differentiable or where it is feasible to calculate the derivatives by numerical methods. Furthermore, this method relies on at least one initial point being placed within the basin of the true global minimum, since the algorithm lacks the capability for global exploration.