SMGO：一种数据驱动全局Optimization的集员方法
SMGO: A Set Membership Approach to Data-Driven Global Optimization
摘要：
许多科学和工程应用的特点是非凸优化问题，其中的目标函数不能解析处理，即它是一个黑盒。实例包括通过实验或通过昂贵的有限元模拟的设计优化。为了解决这些问题，使用全局优化例程。这些迭代技术必须权衡利用接近当前最佳点的搜索空间的未知区域的探索。在这方面，提出了一种新的全局优化策略的基础上的集成员（SM）框架。假设费用函数为Lipschitz连续，该方法采用SM概念来决定是否从开采模式切换到勘探模式，反之亦然。由此产生的算法，命名为SMGO（集成员全局优化）。关于收敛性和计算复杂性的理论属性推导，并讨论了实施方面。最后，SMGO的性能进行了评估的一组基准非凸问题，并与其他全局优化方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
