Kriging-based unconstrained global optimization through multi-point sampling
Kriging-based unconstrained global optimization through multi-point sampling
复制标题
通过多点采样基于克里金法的无约束全局优化
DOI：
10.1080/0305215x.2019.1668934
复制
发表时间：
2019-11-06
影响因子：
2.7
通讯作者：
Wu, Yizhong
中科院分区：
文献类型：
--
作者：
Li, Yaohui;Wang, Shuting;Wu, Yizhong
摘要广义有效全局优化（GEGO）方法能够解决代价高昂的黑箱问题。然而，每个周期选择一个采样点可能导致大量的时间消耗和收敛精度的损失。为此，提出了一种基于Kriging的多点无约束全局优化（KMUGO）方法。它扩展了GEGO方法与改进的常数说谎者（CL）的战略。对于每个周期，克里金模型首先由现有的采样数据构建或更新。然后，使用增强的备选CL策略来寻找多个点，这些点将被进一步筛选以确定最终的昂贵评估点。数值算例和工程模拟算例的测试结果表明，KMUGO算法的收敛性优于GEGO算法、基于多点采样方法的克里格法（MPSK）、基于混合自适应元模型的全局优化算法（HAM）和基于克里格法的多点填充搜索准则全局优化算法（KGOMISC）。
ABSTRACT The generalized efficient global optimization (GEGO) method is able to solve expensive black-box problems. However, selecting one sampling point per cycle may result in large time consumption and loss of convergence accuracy. To this end, a kriging-based multi-point unconstrained global optimization (KMUGO) method is proposed. It extends the GEGO method with the improved constant liar (CL) strategy. For each cycle, the kriging model is first constructed or updated by the existing sampled data. Then, the enhanced alternative CL strategy is used to find multiple points, which will be further screened to identify the final expensive-evaluation points. Test results of numerical problems and an engineering simulation case show that KMUGO can deliver better convergence than GEGO, multi-point sampling method-based kriging (MPSK), hybrid and adaptive meta-model-based global optimization (HAM) and the kriging-based global optimization method using multi-point infill search criterion (KGOMISC).