论文标题
SMGO：用于数据驱动的全局优化的设定成员资格方法
SMGO: A Set Membership Approach to Data-Driven Global Optimization
论文作者
论文摘要
许多科学和工程应用都具有非凸优化问题，在这些问题中无法通过分析来处理目标函数，即是黑匣子。示例包括通过实验或通过昂贵的有限元模拟进行设计优化。为了解决这些问题，使用了全局优化程序。这些迭代技术必须通过对搜索空间看不见的地区的探索来取消剥削，接近当前最佳点。在这方面，提出了基于设定会员资格（SM）框架的新的全球优化策略。假设Lipschitz的成本函数连续性，该方法采用SM概念来决定是否从剥削模式切换到探索模式，反之亦然。提出了所得算法，名为SMGO（SET成员全局优化）。得出了有关收敛和计算复杂性的理论特性，并讨论了实施方面。最后，在一组基准非凸问题上评估了SMGO性能，并将其与其他全球优化方法进行了比较。
Many science and engineering applications feature non-convex optimization problems where the objective function can not be handled analytically, i.e. it is a black box. Examples include design optimization via experiments, or via costly finite elements simulations. To solve these problems, global optimization routines are used. These iterative techniques must trade-off exploitation close to the current best point with exploration of unseen regions of the search space. In this respect, a new global optimization strategy based on a Set Membership (SM) framework is proposed. Assuming Lipschitz continuity of the cost function, the approach employs SM concepts to decide whether to switch from an exploitation mode to an exploration one, and vice-versa. The resulting algorithm, named SMGO (Set Membership Global Optimization) is presented. Theoretical properties regarding convergence and computational complexity are derived, and implementation aspects are discussed. Finally, the SMGO performance is evaluated on a set of benchmark non-convex problems and compared with those of other global optimization approaches.
