Also, this method proved to be relatively insensitive to the selected parameters. In the case of the DE algorithm, the best1bin strategy was able to achieve fast convergence but had a tendency to get caught in local minima of multimodal functions. Changing the differential evolutionary strategy from best1bin to rand1bin may increase the number of function evaluations but help to more reliably find the global minimum. For CMA-ES, a vastly improved result was found after only a few iterations. Still, high-complex benchmark functions such as Schwefel or Xin-She Yang N.4 bore a high risk of premature convergence.