An enhancement of constraint feasibility in BPN based approximate optimization

Jongsoo Lee, Heeseok Jeong, Dong Hoon Choi, Vitali Volovoi, Dimitri Mavris

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)2147-2160
Number of pages14
JournalComputer Methods in Applied Mechanics and Engineering
Issue number17-20
Publication statusPublished - 2007 Mar 15

Bibliographical note

Funding Information:
This research was supported by The Center of Innovative Design Optimization Technology, Korea Science and Engineering Foundation.

All Science Journal Classification (ASJC) codes

  • Computational Mechanics
  • Mechanics of Materials
  • Mechanical Engineering
  • Physics and Astronomy(all)
  • Computer Science Applications


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