A lower confidence bounding approach based on the coefficient of variation for expensive global design optimization. Issue 3 (8th April 2019)

• Record Type: Journal Article
• Authors: Cheng, Ji; Jiang, Ping; Zhou, Qi; Hu, Jiexiang; Yu, Tao; Shu, Leshi; Shao, Xinyu
• Is Part Of: Engineering computations. Volume 36:Issue 3(2019)
• Page Start: 830
• Page End: 849
• Publication Date: 2019-04-08
• Subjects: Kriging -- Coefficient of variation -- Lower confidence bounding -- Metamodel-based design optimization; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-08-2018-0390
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: Engineering 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/approach: In 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. Findings: Ten 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 implications: The proposed approach exhibits high efficiency and robustness in engineering design optimization involving computational simulations. Originality/value: CV-LCB approach can balance the exploration and exploitation objectively.