Computational efficiency in simulation-based optimization algorithms is essential when the system objective functions are expensive to evaluate and computational resources are limited. This article proposes a hybrid Bayesian BFGS algorithm (HB2O) to address this efficiency problem. An adaptive expected improvement (AEI) acquisition function is developed to realize a self-adaptive sampling strategy by dynamically balancing the design space exploration and exploitation. A series of computational experiments is conducted on a diverse set of test functions to benchmark the optimization performance of the HB2O against six commonly used alternative optimizers, and to validate the effectiveness of AEI against four alternative acquisition functions. The computational results show that the proposed HB2O can robustly converge on the functions’ optima with limited simulation samples, and it significantly outperforms other optimizers for various test functions. This article provides a sample-efficient solution to complex optimization problems where taking a large number of system simulations is computationally prohibitive.

当系统目标函数评估成本高且计算资源有限时，基于仿真的优化算法的计算效率至关重要。本文提出了一种混合贝叶斯BFGS算法（HB2O）来解决这个效率问题。开发了自适应期望改进（AEI）获取功能，通过动态平衡设计空间探索和开发来实现自适应采样策略。对一组不同的测试函数进行了一系列计算实验，以针对六种常用替代优化器对 HB2O 的优化性能进行基准测试，并验证 AEI 对四种替代采集函数的有效性。计算结果表明，所提出的 HB2O 可以在有限的模拟样本下稳健地收敛于函数的最优值，并且在各种测试函数方面明显优于其他优化器。本文为复杂的优化问题提供了一个样本高效的解决方案，在这些问题中，进行大量系统模拟在计算上是令人望而却步的。