Optimization by moving ridge functions: derivative-free optimization for computationally intensive functions
Optimization by moving ridge functions: derivative-free optimization for computationally intensive functions
复制标题
DOI：
10.1080/0305215x.2021.1886286
复制
发表时间：
2020-07
影响因子：
2.7
通讯作者：
James C. Gross;G. Parks
中科院分区：
文献类型：
--
作者：
James C. Gross;G. Parks
提出了一种新颖的无导数算法，称为移动岭函数优化 (OMoRF)，用于无约束和有界约束优化。该算法将信任域方法与基于输出的降维相结合，以加速基于模型的优化策略的收敛。当信任区域穿过函数域时，降维子空间会更新，从而允许 OMoRF 应用于没有已知全局低维结构的函数。此外，其较低的计算要求使其在优化高维函数时能够取得快速进展。其性能在一组中高维测试问题和高维设计优化问题上进行了检验。结果表明，OMorF 与其他常见的无导数优化方法相比具有优势，即使对于不知道底层全局低维结构的函数也是如此。
A novel derivative-free algorithm, called optimization by moving ridge functions (OMoRF), for unconstrained and bound-constrained optimization is presented. This algorithm couples trust region methodologies with output-based dimension reduction to accelerate convergence of model-based optimization strategies. The dimension-reducing subspace is updated as the trust region moves through the function domain, allowing OMoRF to be applied to functions with no known global low-dimensional structure. Furthermore, its low computational requirement allows it to make rapid progress when optimizing high-dimensional functions. Its performance is examined on a set of test problems of moderate to high dimension and a high-dimensional design optimization problem. The results show that OMoRF compares favourably with other common derivative-free optimization methods, even for functions in which no underlying global low-dimensional structure is known.