期刊
OPTIMIZATION LETTERS
卷 11, 期 5, 页码 895-913出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s11590-016-1028-2
关键词
Grey-box optimization; Surrogate modeling; Variable selection; Derivative-free optimization; General constraints; Nonlinear programming
资金
- National Science Foundation [CBET-0827907, CBET-1263165]
The algorithmic framework ARGONAUT is presented for the global optimization of general constrained grey-box problems. ARGONAUT incorporates variable selection, bounds tightening and constrained sampling techniques, in order to develop accurate surrogate representations of unknown equations, which are globally optimized. ARGONAUT is tested on a large set of test problems for constrained global optimization with a large number of input variables and constraints. The performance of the presented framework is compared to that of existing techniques for constrained derivative-free optimization.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据