Next Article in Journal
Statistical Analysis of Acoustic Emission in Uniaxial Compression of Tectonic and Non-Tectonic Coal
Previous Article in Journal
Groundwater Level Fluctuation Analysis in a Semi-Urban Area Using Statistical Methods and Data Mining Techniques—A Case Study in Wrocław, Poland
Previous Article in Special Issue
A Coordination Space Model for Assemblability Analysis and Optimization during Measurement-Assisted Large-Scale Assembly
Open AccessArticle

An Entropy Weight-Based Lower Confidence Bounding Optimization Approach for Engineering Product Design

1
School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
2
Wuhan Second Ship Design and Research Institute, Wuhan 430064, China
3
Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration (CISSE), Shanghai 200240, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(10), 3554; https://doi.org/10.3390/app10103554
Received: 15 January 2020 / Revised: 13 May 2020 / Accepted: 18 May 2020 / Published: 21 May 2020
(This article belongs to the Special Issue Computer-Aided Manufacturing and Design)
The optimization design of engineering products involving computationally expensive simulation is usually a time-consuming or even prohibitive process. As a promising way to relieve computational burden, adaptive Kriging-based design optimization (AKBDO) methods have been widely adopted due to their excellent ability for global optimization under limited computational resource. In this paper, an entropy weight-based lower confidence bounding approach (EW-LCB) is developed to objectively make a trade-off between the global exploration and the local exploitation in the adaptive optimization process. In EW-LCB, entropy theory is used to measure the degree of the variation of the predicted value and variance of the Kriging model, respectively. Then, an entropy weight function is proposed to allocate the weights of exploration and exploitation objectively and adaptively based on the values of information entropy. Besides, an index factor is defined to avoid the sequential process falling into the local regions, which is associated with the frequencies of the current optimal solution. To demonstrate the effectiveness of the proposed EW- LCB method, several numerical examples with different dimensions and complexities and the lightweight optimization design problem of an underwater vehicle base are utilized. Results show that the proposed approach is competitive compared with state-of-the-art AKBDO methods considering accuracy, efficiency, and robustness. View Full-Text
Keywords: Kriging; lower confidence bounding; entropy theory; product design; simulation-based design optimization Kriging; lower confidence bounding; entropy theory; product design; simulation-based design optimization
Show Figures

Figure 1

MDPI and ACS Style

Qian, J.; Yi, J.; Zhang, J.; Cheng, Y.; Liu, J. An Entropy Weight-Based Lower Confidence Bounding Optimization Approach for Engineering Product Design. Appl. Sci. 2020, 10, 3554.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop