Data-driven Spatial Branch-and-bound Algorithm for Box-constrained Simulation-based Optimization
Data-driven Spatial Branch-and-bound Algorithm for Box-constrained Simulation-based Optimization
复制标题
用于基于框约束仿真的优化的数据驱动空间分支定界算法
DOI：
10.1007/s10898-021-01045-8
复制
发表时间：
2021
影响因子：
1.8
通讯作者：
Boukouvala, F.
中科院分区：
文献类型：
--
作者：
Zhai, J.;Boukouvala, F.
在定量分析和决策中使用复杂的计算机模拟的能力在科学和工程中是非常需要的，与计算能力和第一原理知识的发展速度相同。由于仿真模型的复杂性，直接嵌入基于方程的优化求解器可能是不切实际的，并且通常需要数据驱动的优化技术。在这项工作中，我们提出了一种新的数据驱动的空间分支定界算法的模拟为基础的优化问题与框约束，旨在一致的全局收敛的解决方案。本文的主要贡献是引入了数据驱动的数据和代理函数的凸低估的概念，这是一个空间的分支定界算法。该算法展示了一个说明性的例子，然后通过大量的基准问题的计算实验进行了广泛的研究。
The ability to use complex computer simulations in quantitative analysis and decision-making is highly desired in science and engineering, at the same rate as computation capabilities and first-principle knowledge advance. Due to the complexity of simulation models, direct embedding of equation-based optimization solvers may be impractical and data-driven optimization techniques are often needed. In this work, we present a novel data-driven spatial branch-and-bound algorithm for simulation-based optimization problems with box constraints, aiming for consistent globally convergent solutions. The main contribution of this paper is the introduction of the concept data-driven convex underestimators of data and surrogate functions, which are employed within a spatial branch-and-bound algorithm. The algorithm is showcased by an illustrative example and is then extensively studied via computational experiments on a large set of benchmark problems.