Enhanced Morris for the extraction of significant parameters in high-dimensional design optimization

Yuan, Pei Pei; Cheng, Jie-Zhi; Liu, Zhenyu; Liu, Daxin; Tan, Jianrong

doi:10.1007/s12206-016-1024-6

Cited by 8 publications

(1 citation statement)

References 28 publications

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“…A good sampling scheme (or DOE) should have a good trade-off between the computational cost and accuracy. It should be noted that DOE is not only used to build the surrogate model, it is also widely used in sensitivity analysis to extract the most important parameters from a large group of design parameters (Yuan et al, 2016). In this study, the original model is realized by the deterministic computer simulator, so the output will be the same if the simulator runs twice at the same input values.…”

Section: Introductionmentioning

confidence: 99%

A new sequential sampling method for constructing the high-order polynomial surrogate models

Luo

Zhang

et al. 2018

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Purpose This paper aims to study the sampling methods (or design of experiments) which have a large influence on the performance of the surrogate model. To improve the adaptability of modelling, a new sequential sampling method termed as sequential Chebyshev sampling method (SCSM) is proposed in this study. Design/methodology/approach The high-order polynomials are used to construct the global surrogated model, which retains the advantages of the traditional low-order polynomial models while overcoming their disadvantage in accuracy. First, the zeros of Chebyshev polynomials with the highest allowable order will be used as sampling candidates to improve the stability and accuracy of the high-order polynomial model. In the second step, some initial sampling points will be selected from the candidates by using a coordinate alternation algorithm, which keeps the initial sampling set uniformly distributed. Third, a fast sequential sampling scheme based on the space-filling principle is developed to collect more samples from the candidates, and the order of polynomial model is also updated in this procedure. The final surrogate model will be determined as the polynomial that has the largest adjusted R-square after the sequential sampling is terminated. Findings The SCSM has better performance in efficiency, accuracy and stability compared with several popular sequential sampling methods, e.g. LOLA-Voronoi algorithm and global Monte Carlo method from the SED toolbox, and the Halton sequence. Originality/value The SCSM has good performance in building the high-order surrogate model, including the high stability and accuracy, which may save a large amount of cost in solving complicated engineering design or optimisation problems.

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Section: Introductionmentioning

confidence: 99%