标题： 系统架构优化策略：处理昂贵的分层问题 显示英文标题

标题： System Architecture Optimization Strategies: Dealing with Expensive Hierarchical Problems

摘要： 针对手头问题选择合适的系统架构具有挑战性，因为设计空间大且在设计过程早期存在高度不确定性。 将架构过程表述为优化问题可能可以缓解这些挑战。 本研究调查了求解系统架构优化（SAO）问题的策略：这些问题昂贵、黑盒、分层、混合离散、受限且多目标，并可能受到隐藏约束的影响。 定义了填充率、校正率、校正分数和最大速率多样性度量来表征分层设计空间。 本研究考虑了两类用于SAO的优化算法：多目标进化算法（MOEA），例如NSGA-II，以及贝叶斯优化（BO）算法。 提出了一种新的高斯过程核，能够对分层分类变量进行建模，扩展了之前对连续和整数分层变量建模的工作。 接下来，开发了一种分层采样算法，该算法利用设计空间的层次结构按活动设计变量对设计向量进行分组。 然后证明，在优化算法中集成更多层次信息可使BO算法获得更好的优化结果。 使用了几个现实世界中的单目标和多目标测试问题进行研究。 最后，将BO算法应用于喷气发动机架构优化问题。 本研究表明，所开发的BO算法能以比NSGA-II少一个数量级的功能评估次数有效解决问题。 本研究中使用的算法和问题在开源Python库SBArchOpt中实现。 显示英文摘要

摘要： Choosing the right system architecture for the problem at hand is challenging due to the large design space and high uncertainty in the early stage of the design process. Formulating the architecting process as an optimization problem may mitigate some of these challenges. This work investigates strategies for solving System Architecture Optimization (SAO) problems: expensive, black-box, hierarchical, mixed-discrete, constrained, multi-objective problems that may be subject to hidden constraints. Imputation ratio, correction ratio, correction fraction, and max rate diversity metrics are defined for characterizing hierar chical design spaces. This work considers two classes of optimization algorithms for SAO: Multi-Objective Evolutionary Algorithms (MOEA) such as NSGA-II, and Bayesian Optimization (BO) algorithms. A new Gaussian process kernel is presented that enables modeling hierarchical categorical variables, extending previous work on modeling continuous and integer hierarchical variables. Next, a hierarchical sampling algorithm that uses design space hierarchy to group design vectors by active design variables is developed. Then, it is demonstrated that integrating more hierarchy information in the optimization algorithms yields better optimization results for BO algorithms. Several realistic single-objective and multi-objective test problems are used for investigations. Finally, the BO algorithm is applied to a jet engine architecture optimization problem. This work shows that the developed BO algorithm can effectively solve the problem with one order of magnitude less function evaluations than NSGA-II. The algorithms and problems used in this work are implemented in the open-source Python library SBArchOpt.