Ensemble of Kriging with Multiple Kernel Functions for Engineering Design Optimization

Abstract A monomial-based method for solving systems of algebraic nonlinear equations is presented. The method uses the arithmetic-geometric mean inequality to construct a system of monomial equations that approximates the system of nonlinear equations. This “monomial method” is closely related to Newton’s method, yet exhibits many special properties not shared by Newton’s method that enhance performance. These special properties are discussed in relation to engineering design optimization.

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A Simple Multi-population Evolutionary Algorithm Using PSO Strategy for Constrained Engineering Design Optimization

International Journal of Digital Content Technology and its Applications ◽

10.4156/jdcta.vol6.issue13.56 ◽

2012 ◽

Vol 6 (13) ◽

pp. 532-541 ◽

Cited By ~ 1

Author(s):

Yi Liu ◽

Caihong Mu ◽

Weidong Kou ◽

Jing Liu

Keyword(s):

Evolutionary Algorithm ◽

Design Optimization ◽

Engineering Design ◽

Engineering Design Optimization

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A lower confidence bounding approach based on the coefficient of variation for expensive global design optimization

Engineering Computations ◽

10.1108/ec-08-2018-0390 ◽

2019 ◽

Vol 36 (3) ◽

pp. 830-849 ◽

Cited By ~ 7

Author(s):

Ji Cheng ◽

Ping Jiang ◽

Qi Zhou ◽

Jiexiang Hu ◽

Tao Yu ◽

...

Keyword(s):

Design Optimization ◽

Engineering Design ◽

Computational Efficiency ◽

Coefficient Of Variation ◽

Computational Simulations ◽

Exploration And Exploitation ◽

Content Type ◽

Degree Of Dispersion ◽

Lower Confidence ◽

Engineering Design Optimization

PurposeEngineering design optimization involving computational simulations is usually a time-consuming, even computationally prohibitive process. To relieve the computational burden, the adaptive metamodel-based design optimization (AMBDO) approaches have been widely used. This paper aims to develop an AMBDO approach, a lower confidence bounding approach based on the coefficient of variation (CV-LCB) approach, to balance the exploration and exploitation objectively for obtaining a global optimum under limited computational budget.Design/methodology/approachIn the proposed CV-LCB approach, the coefficient of variation (CV) of predicted values is introduced to indicate the degree of dispersion of objective function values, while the CV of predicting errors is introduced to represent the accuracy of the established metamodel. Then, a weighted formula, which takes the degree of dispersion and the prediction accuracy into consideration, is defined based on the already-acquired CV information to adaptively update the metamodel during the optimization process.FindingsTen numerical examples with different degrees of complexity and an AIAA aerodynamic design optimization problem are used to demonstrate the effectiveness of the proposed CV-LCB approach. The comparisons between the proposed approach and four existing approaches regarding the computational efficiency and robustness are made. Results illustrate the merits of the proposed CV-LCB approach in computational efficiency and robustness.Practical implicationsThe proposed approach exhibits high efficiency and robustness in engineering design optimization involving computational simulations.Originality/valueCV-LCB approach can balance the exploration and exploitation objectively.

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