论文标题
通过平行的全球和本地结构增强了全球优化
Enhanced Global Optimization with Parallel Global and Local Structures
论文作者
论文摘要
实际上，实时控制系统的客观功能可以具有多个局部最小值，也可以在功能空间上发生巨大变化，从而难以优化。为了有效地优化此类系统，在本文中，我们开发了一个并行的全局优化框架，该框架将直接搜索方法与贝叶斯并行优化相结合。它由迭代的全球和本地搜索组成，该搜索在整个全球空间中广泛搜索有前途的地区，然后有效利用每个本地有前途的地区。我们证明了所提出的框架的渐近收敛特性，并进行了广泛的数值比较，以说明其经验性能。
In practice, objective functions of real-time control systems can have multiple local minimums or can dramatically change over the function space, making them hard to optimize. To efficiently optimize such systems, in this paper, we develop a parallel global optimization framework that combines direct search methods with Bayesian parallel optimization. It consists of an iterative global and local search that searches broadly through the entire global space for promising regions and then efficiently exploits each local promising region. We prove the asymptotic convergence properties of the proposed framework and conduct an extensive numerical comparison to illustrate its empirical performance.
