An extensive numerical benchmark study of deterministic vs. stochastic derivative-free global optimization algorithms
Abstract
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.
Cite
@article{arxiv.2209.05759,
title = {An extensive numerical benchmark study of deterministic vs. stochastic derivative-free global optimization algorithms},
author = {Linas Stripinis and Remigijus Paulavičius},
journal= {arXiv preprint arXiv:2209.05759},
year = {2022}
}