论文标题
确定性与随机导数的全局优化算法的广泛数值基准研究
An extensive numerical benchmark study of deterministic vs. stochastic derivative-free global optimization algorithms
论文作者
论文摘要
无衍生化的全球优化研究正在积极开发中，并且今天有许多解决方案技术可用。因此，必须保持最新的先前和新兴算法的实验比较。本文考虑了解决边界约束的，可能是黑盒全局优化问题的解决方案。它比较了64个无衍生化的确定性算法与经典和最先进的随机求解器。在确定性的方面，特别重点是直接类型，近年来，已经取得了重大进展。一组著名的GKLS发电机产生的800个测试问题和DirectGolib V1.2收集的397个传统测试问题在计算研究中使用。进行了239400多个求解器运行，需要超过531天的单个CPU时间才能完成它们。已经发现，确定性算法在GKLS型和低维问题上表现出色，而随机算法在更高的维度上已显示出更有效的效率。
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.
