序列Kriging仿真优化方法综述  

Review on Simulation Optimization Method of Sequential Kriging

作　　者：师路欢[1] 李耀辉[1] 吴义忠[2] 王书亭[2] SHI Lu-huan;LI Yao-hui;WU Yi-zhong;WANG Shu-ting(School of Mechano-Electronic Engineering in Xuchang University,Henan Xuchang 461000,China;National CAD Software Engineering Centre in Huazhong University of Science and Technology,Hubei Wuhan 430074,China)

机构地区：[1]许昌学院电气(机电)工程学院,河南许昌461000 [2]华中科技大学国家企业信息化CAD应用支撑软件工程技术研究中心,湖北武汉430074

出　　处：《机械设计与制造》2021年第11期279-286,共8页Machinery Design & Manufacture

基　　金：国家自然科学基金项目—基于Kriging模型的仿真优化方法关键技术研究(51775472);河南高校科技创新人才项目—复杂产品基于Kriging模型的仿真优化设计(21HASTIT027);河南省高校青年骨干教师计划—基于Kriging仿真优化方法的研究与应用(2020GGJS209)。

摘　　要：通过深度学习来挖掘设计变量、目标参数与Kriging模型之间内在关系的序列Kriging仿真优化方法已成为基于元模型优化的研究前沿和热点。但仿真优化过程中存在建模效率较低、收敛精度不高、多点采样的并行仿真难以实现等问题。如何在少量昂贵仿真估值条件下提高优化效率和收敛精度是序列Kriging仿真优化方法研究的主要内容。为此,对序列Kriging的近似建模方法、无约束优化、多点并行优化以及约束优化进行综述,介绍经典优化方法、若干改进及相应工具包,并展望所面临的问题和挑战。Sequential Kriging simulation optimization method for mining the intrinsic relationship between design variables,tar⁃get parameters and Kriging through deep learning has become a research frontier and hotspot.However,there are problems such as low modeling efficiency,convergence precision,and parallel simulation of multi-point sampling.How to improve the optimiza⁃tion efficiency and convergence precision under a few expensive evaluation conditions is the main research content.To this end,the approximate modeling of Kriging,unconstrained,multi-point parallel and constrained optimization are reviewed.The classi⁃cal optimization method,several improvements and corresponding toolkits are introduced,and the problems and challenges are prospected.

关 键 词：试验设计 Kriging元模型 仿真优化 加点采样准则 

分 类 号：TH16[机械工程—机械制造及自动化] TPH122[自动化与计算机技术]