An algorithmic framework for the optimization of computationally expensive bi-fidelity black-box problems. Issue 2 (2nd April 2020)

• Record Type: Journal Article
• Authors: Müller, Juliane
• Is Part Of: Infor. Volume 58:Issue 2(2020)
• Page Start: 264
• Page End: 289
• Publication Date: 2020-04-02
• Subjects: Global optimization -- black-box optimization -- multi-fidelity optimization -- surrogate models; Operations research -- Periodicals; Electronic data processing -- Periodicals; Systems engineering -- Periodicals; Systems engineering; Electronic data processing; Periodicals; 003.05
• DOI: 10.1080/03155986.2019.1607810
• Languages: English
• ISSNs: 0315-5986

Abstract

Abstract: We introduce an algorithm for the optimization of problems whose objective functions are evaluated by computationally expensive black-box simulations and for which an analytic description of the objective and its derivatives are not available. We consider the case where two levels of simulation model fidelity are available, namely a high fidelity model that is computationally very expensive to evaluate, and a low fidelity model that is less accurate and computationally cheaper but still time consuming. The computational effort is alleviated by using computationally cheap surrogate models that approximate the simulations at both fidelity levels. The local correlation between both fidelity surrogate models determines when the low fidelity model can be trusted for making sampling decisions for the high fidelity model. In the numerical experiments we investigate how well our algorithm responds to problems whose objective function fidelity levels are well correlated and badly correlated. We study how different initial design strategies and parameter settings impact the performance of the algorithm. The results show that our algorithm actively learns from the local correlation computations how well suited the low fidelity model is for making sampling decisions and it ignores the low fidelity model if the correlation is too low.