代理辅助的可靠性设计优化：综述和一个新的通用框架
Surrogate-assisted reliability-based design optimization: a survey and a new general framework
摘要：
基于可靠性的设计优化（RBDO）是一个活跃的研究领域，其贡献越来越多。RBDO是一个结合了优化和可靠性分析的复杂问题，已经提出了许多方法来解决RBDO。经典的方法是基于近似方法，并已在综述论文中进行了分类。在本文中，我们首先回顾经典的方法，如FORM的近似方法的基础上，也是最近的方法，依赖于代理建模和蒙特卡罗模拟。然后，我们提出了一个通用的框架RBDO问题的解决方案，包括三个独立的块，即自适应代理建模，可靠性分析和优化。这些块彼此之间是非侵入性的，并且可以独立地插入框架中。在讨论了需要注意的框架，以产生强大的解决方案，以各种类型的问题的数值考虑，后者被应用到三个例子（使用两个分析功能和有限元模型）。克里格和支持向量机连同他们自己的主动学习计划被认为是在代理模型块。在可靠性分析方面，所提出的框架说明使用粗蒙特卡罗和子集模拟。最后，协方差矩阵自适应进化方案（CMA-ES），全局搜索算法，或序列二次规划（SQP），局部梯度为基础的方法，用于优化块。基准研究的结果比较表明，所提出的框架的有效性和效率。
英文摘要：
Reliability-based design optimization (RBDO) is an active field of research with an ever increasing number of contributions. Numerous methods have been proposed for the solution of RBDO, a complex problem that combines optimization and reliability analysis. Classical approaches are based on approximation methods and have been classified in review papers. In this paper, we first review classical approaches based on approximation methods such as FORM, and also more recent methods that rely upon surrogate modelling and Monte Carlo simulation. We then propose a general framework for the solution of RBDO problems that includes three independent blocks, namely adaptive surrogate modelling, reliability analysis and optimization. These blocks are non-intrusive with respect to each other and can be plugged independently in the framework. After a discussion on numerical considerations that require attention for the framework to yield robust solutions to various types of problems, the latter is applied to three examples (using two analytical functions and a finite element model). Kriging and support vector machines together with their own active learning schemes are considered in the surrogate model block. In terms of reliability analysis, the proposed framework is illustrated using both crude Monte Carlo and subset simulation. Finally, the covariance-matrix adaptation - evolution scheme (CMA-ES), a global search algorithm, or sequential quadratic programming (SQP), a local gradient-based method, are used in the optimization block. The comparison of the results to benchmark studies show the effectiveness and efficiency of the proposed framework.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
