论文标题
通过移动脊功能进行优化：计算密集型功能的无衍生化优化
Optimization by moving ridge functions: Derivative-free optimization for computationally intensive functions
论文作者
论文摘要
提出了一种新颖的无衍生算法，即通过移动脊函数（OMORF）进行优化，以实现不受限制和约束的优化。该算法将信任区域方法与基于输出的维度缩小相结合，以加速基于模型的优化策略的收敛。随着信任区域穿过函数域的移动，更新尺寸的子空间会更新，从而使Omorf应用于没有已知的全局低维度结构的功能。此外，其低计算需求使其在优化高维函数时可以快速进步。在一组中等至高维和高维设计优化问题的测试问题上检查了其性能。结果表明，OMORF与其他常见无衍生的优化方法相比，即使对于没有潜在的全局低维结构的功能也是如此。
A novel derivative-free algorithm, 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 to other common derivative-free optimization methods, even for functions in which no underlying global low-dimensional structure is known.
