Asset Details
Do you wish to reserve the book?
Reliability-based design optimization using adaptive surrogate model and importance sampling-based modified SORA method
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Reliability-based design optimization using adaptive surrogate model and importance sampling-based modified SORA method
Do you wish to request the book?
Reliability-based design optimization using adaptive surrogate model and importance sampling-based modified SORA method
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Reliability-based design optimization using adaptive surrogate model and importance sampling-based modified SORA method
2021
Overview
Reliability-based design optimization (RBDO) has been an important research field with the increasing demand for product reliability in practical applications. This paper presents a new RBDO method combining adaptive surrogate model and Importance Sampling-based Modified Sequential Optimization and Reliability Assessment (IS-based modified SORA) method, which aims to reduce the number of calls to the expensive objective function and constraint functions in RBDO. The proposed method consists of three key stages. First, the samples are sequentially selected to construct Kriging models with high classification accuracy for each constraint function. Second, the samples are obtained by Markov Chain Monte Carlo in the safety domain of design space. Then, another Kriging model for the objective function is sequentially constructed by adding suitable samples to update the Design of Experiment (DoE) of the objective function. Third, the expensive objective and constraint functions of the original optimization problem are replaced by the surrogate models. Then, the IS-based modified SORA method is performed to decouple reliability optimization problem into a series of deterministic optimization problems that are solved by a Genetic Algorithm. Several examples are adopted to verify the proposed method. The optimization results show that the proposed method can reduce the number of calls to the original objective function and constraint functions without loss of precision compared to the alternative methods, which illustrates the efficiency and accuracy of the proposed method.
Publisher
Springer Nature B.V
