Global optimization of large-scale, complex systems such as multi-physics black-box simulations and real-world industrial systems is important but challenging. This work presents a novel Surrogate-Based Optimization framework based on Clustering, SBOC for global optimization of such systems, which can be used with any surrogate modeling technique. At each iteration, it uses a single surrogate model for the entire domain, employs k-means clustering to identify unexplored domain, and exploits a local region around the surrogate optimum to potentially add three new sample points in the domain. SBOC has been tested against sixteen promising benchmarking algorithms using 52 analytical test functions of varying input dimensionalities and shape profiles. It successfully identified a global minimum for most test functions with substantially lower computational effort than other algorithms. It worked especially well on test functions with four or more input variables. It was also among the top six algorithms in approaching a global minimum closely. Overall, SBOC is a robust, reliable, and efficient algorithm for global optimization of box-constrained systems.

翻译：大规模复杂系统（如多物理场黑箱仿真和实际工业系统）的全局优化具有重要意义，但极具挑战性。本研究提出一种新颖的基于聚类的代理优化框架——SBOC，用于此类系统的全局优化，该框架可与任何代理建模技术结合使用。在每次迭代中，SBOC采用单一代理模型覆盖整个定义域，利用k均值聚类识别未探索区域，并围绕代理模型最优解开发局部区域，从而可能在定义域中新增三个样本点。通过52个具有不同输入维度和形态特征的解析测试函数，SBOC与十六种前沿基准算法进行了对比测试。结果表明，在大多数测试函数中，SBOC能以显著低于其他算法的计算成本成功识别全局最小值。该算法在四维及以上输入变量的测试函数中表现尤为突出，同时在逼近全局最小值的精度方面位列所有算法前六名。总体而言，SBOC是一种鲁棒性强、可靠性高且计算高效的箱约束系统全局优化算法。