Design optimization by integrating limited simulation data and shape engineering knowledge with Bayesian optimization (BO-DK4DO)

Jia Hao (), Mengying Zhou (), Guoxin Wang (), Liangyue Jia () and Yan Yan ()
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Jia Hao: Beijing Institute of Technology
Mengying Zhou: Beijing Institute of Technology
Guoxin Wang: Beijing Institute of Technology
Liangyue Jia: Beijing Institute of Technology
Yan Yan: Beijing Institute of Technology

Journal of Intelligent Manufacturing, 2020, vol. 31, issue 8, No 15, 2049-2067

Abstract: Abstract Surrogate models have been widely studied for optimization tasks in the domain of engineering design. However, the expensive and time-consuming simulation cycles needed for complex products always result in limited simulation data, which brings a challenge for building high accuracy surrogate models because of the incomplete information contained in the limited simulation data. Therefore, a method that builds a surrogate model and conducts design optimization by integrating limited simulation data and engineering knowledge through Bayesian optimization (BO-DK4DO) is presented. In this method, the shape engineering knowledge is considered and used as derivative information which is integrated with the limited simulation data with a Gaussian process (GP). Then the GP is updated sequentially by sampling new simulation data and the optimal design solutions are found by maximizing the GP. The aim of BO-DK4DO is to significantly reduce the required number of computer simulations for finding optimal design solutions. The BO-DK4DO is verified by using benchmark functions and an engineering design problem: hot rod rolling. In all scenarios, the BO-DK4DO shows faster convergence rate than the general Bayesian optimization without integrating engineering knowledge, which means the required amount of data is decreased.

Keywords: Bayesian optimization; Limited simulation data; Engineering knowledge; Surrogate model; Design optimization (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10845-020-01551-8

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