System Architecture Optimization Strategies: Dealing with Expensive Hierarchical Problems

TL;DR

This paper develops and compares hierarchical optimization strategies, especially Bayesian Optimization with a new kernel, for complex system architecture problems, demonstrating significant efficiency gains over traditional methods.

Contribution

Introduces a new Gaussian process kernel for hierarchical categorical variables and a hierarchical sampling algorithm, improving Bayesian Optimization for complex architecture problems.

Findings

BO with the new kernel outperforms NSGA-II in fewer evaluations

Hierarchical information integration improves optimization results

Open-source SBArchOpt library implements the proposed methods

Abstract

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.