An Overview of Adaptive-Surrogate-Model-Assisted Methods for Reliability-Based Design Optimization

Chunyan Ling*, Way Kuo, Min Xie

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

49 Citations (Scopus)

Abstract

Reliability-based design optimization (RBDO) is one of the most crucial techniques in complex and reliability-critical engineering systems. This has been a research hotspot over the past few decades. RBDO allows us to take into consideration the uncertainties from various sources, during the early design stage. It provides a design that satisfies various constraints as well as the reliability of the designed system performing the expected functions. Such capabilities can serve as countermeasures against the foreseeable uncertainties in the manufacturing or application process. Following a short preliminary overview of RBDO as well as the canonical strategies, this article sets out to review how surrogate models have been explored to streamline RBDO. This is done through a systematic study, outlining their respective advantages as well as disadvantages, and discussing the problems that need to be solved.
Original languageEnglish
Pages (from-to)1243-1264
JournalIEEE Transactions on Reliability
Volume72
Issue number3
Online published5 Sept 2022
DOIs
Publication statusPublished - Sept 2023

Funding

This work was supported in part by the Hong Kong Institute for Advanced Study, in part by the National Natural Science Foundation of China under Grant 71971181 and Grant 72032005, in part by the Research Grant Council of Hong Kong under Grant 11203519 and Grant 11200621, in part by the Hong Kong Innovation and Technology Commission (InnoHK Project CIMDA), and in part by the Hong Kong Institute of Data Science under Grant 9360163

Research Keywords

  • Design optimization
  • Indexes
  • Optimization
  • Probabilistic logic
  • Reliability
  • Reliability engineering
  • reliability-based design optimization
  • surrogate model
  • system
  • uncertainty

RGC Funding Information

  • RGC-funded

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