  In the field of derivative-free optimization, both of its main branches, the
deterministic and nature-inspired techniques, experienced in recent years
substantial advancement. In this paper, we provide an extensive computational
comparison of selected methods from each of these branches. The chosen
representatives were either standard and well-utilized methods, or the
best-performing methods from recent numerical comparisons. The computational
comparison was performed on five different benchmark sets and the results were
analyzed in terms of performance, time complexity, and convergence properties
of the selected methods. The results showed that, when dealing with situations
where the objective function evaluations are relatively cheap, the
nature-inspired methods have a significantly better performance than their
deterministic counterparts. However, in situations when the function
evaluations are costly or otherwise prohibited, the deterministic methods might
provide more consistent and overall better results.
