An enhancement of constraint feasibility in BPN based approximate optimization
- Authors
- Lee, Jongsoo; Jeong, Heeseok; Choi, Dong-Hoon; Volovoi, Vitali; Mavris, Dimitri
- Issue Date
- Mar-2007
- Publisher
- ELSEVIER SCIENCE SA
- Keywords
- back-propagation neural networks; inequality constraints; constrained approximate optimization; genetic algorithm
- Citation
- COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, v.196, no.17-20, pp.2147 - 2160
- Indexed
- SCIE
SCOPUS
- Journal Title
- COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Volume
- 196
- Number
- 17-20
- Start Page
- 2147
- End Page
- 2160
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/180364
- DOI
- 10.1016/j.cma.2006.11.005
- ISSN
- 0045-7825
- Abstract
- Back-propagation neural networks (BPN) have been extensively used as global approximation tools in the context of approximate optimization. A traditional BPN is normally trained by minimizing the absolute difference between target outputs and approximate outputs. When BPN is used as a meta-model for inequality constraint function, approximate optimal solutions are sometimes actually infeasible in a case where they are active at the constraint boundary. The paper explores the development of the efficient BPN based meta-model that enhances the constraint feasibility of approximate optimal solution. The BPN based meta-model is optimized via exterior penalty method to optimally determine interconnection weights between layers in the network. The proposed approach is verified through a simple mathematical function and a ten-bar planar truss problem. For constrained approximate optimization, design of rotor blade is conducted to support the proposed strategies.
- Files in This Item
-
Go to Link
- Appears in
Collections - 서울 공과대학 > 서울 건설환경공학과 > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.