The rank transformation applied to a multi-univariate method of global optimization

The incorporation of the rank transformation with an existing multivariate method of global optimization is presented. By transforming the objective function evaluations into ranks and applying Stuckman´s multi-univariate method of global optimization to the ranked data, the resulting method gains the same benefits as the nonparametric method. The method is applied to a standard set of test functions. The application of the rank transformation is shown to significantly reduce the number of function evaluations needed for convergence within a specified tolerance.<>