Abstract
The purpose of this article is to improve the convergence efficiency of the traditional efficient global optimization method. Furthermore, we try a graphics processing unit–based parallel computing method to improve the computing efficiency of the efficient global optimization method for both mathematical and practical engineering problems. First, we propose a multiple-data-based efficient global optimization algorithm instead of the multiple-surrogates-based efficient global optimization algorithm. Second, a novel graphics processing unit–based general-purpose computing technology is adopted to accelerate the solution efficiency of our multiple-data-based efficient global optimization algorithm. Third, a hybrid parallel computing approach using the OpenMP and compute unified device architecture is adopted to further improve the solution efficiency of forward problems in practical application. This is accomplished by integrating the graphics processing unit–based finite element method numerical analysis system into the optimization software. The numerical results show that for the same problem, the optimal result of the multiple-data-based efficient global optimization algorithm is consistently better than the multiple-surrogates-based efficient global optimization algorithm with the same optimization iterations. In addition, the graphics processing unit–based parallel simulation system helps in the reduction of the calculation time for practical engineering problems. The multiple-data-based efficient global optimization method performs stably in both high-order mathematical functions and large-scale nonlinear practical engineering optimization problems. An added benefit is that the computational time and accuracy are no longer obstacles.
Highlights
The purpose of this article is to improve the convergence efficiency of the traditional efficient global optimization method
This article focuses on a modern surrogate-based optimization (SBO) methodology called the efficient global optimization (EGO) algorithm, which is proposed by Jones et al.[14]
According to the above analyses, we found that the multiple-data-based efficient global optimization (MDEGO) algorithm can achieve better results than the original multiplesurrogate efficient global optimization (MSEGO) algorithm
Summary
The purpose of this article is to improve the convergence efficiency of the traditional efficient global optimization method. Within SBO, an iterative process that involves the creation, optimization, and updating of a fast and analytically tractable surrogate model is proposed to replace the direct optimization of the computationally expensive model.[3,4] The surrogate model is used to visualize input–output relationships, search for an optimum candidate, and to analyze the improved design.[5] The common surrogate models include response surfaces,[6] Kriging,[7,8] support vector machines,[9] and space mapping.[10] For the automobile industry, various kinds of surrogate models are widely applied to the structural and parameter optimization of the sheet-metal forming process[11,12] and crash-safety design.[13] This article focuses on a modern SBO methodology called the efficient global optimization (EGO) algorithm, which is proposed by Jones et al.[14] The EGO algorithm favors high convergence efficiency for global optimization by using both estimated prediction and estimated uncertainty to provide the sampling point It is especially good for nonlinear, multimodal functions that often occur in industrial applications. The parallel computing approach has been studied to improve the computing efficiency of optimal design based on the MDEGO algorithm
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