  Research in derivative-free global optimization is under active development,
and many solution techniques are available today. Therefore, the experimental
comparison of previous and emerging algorithms must be kept up to date. This
paper considers the solution to the bound-constrained, possibly black-box
global optimization problem. It compares 64 derivative-free deterministic
algorithms against classic and state-of-the-art stochastic solvers. Among
deterministic ones, a particular emphasis is on DIRECT-type, where, in recent
years, significant progress has been made. A set of 800 test problems generated
by the well-known GKLS generator and 397 traditional test problems from
DIRECTGOLib v1.2 collection are utilized in a computational study. More than
239400 solver runs were carried out, requiring more than 531 days of single CPU
time to complete them. It has been found that deterministic algorithms perform
excellently on GKLS-type and low-dimensional problems, while stochastic
algorithms have shown to be more efficient in higher dimensions.

Optimization and Control;Numerical Analysis;Numerical Analysis