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.

提出了一种新的无导数算法，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方法相结合，以加速基于模型的优化策略的收敛。随着信任区域在函数域中移动，降维子空间会更新，从而允许将 OMoRF 应用于没有已知全局低维结构的函数。此外，它的低计算要求使其在优化高维函数时取得快速进展。在一组中到高维的测试问题和一个高维设计优化问题上检验了它的性能。