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Multi-Objective Composite Panel Optimization Using Machine Learning Classifiers and Genetic Algorithms

[+] Author Affiliations
Kayla Zeliff, Walter Bennette

Air Force Research Laboratory, Rome, NY

Scott Ferguson

North Carolina State University, Raleigh, NC

Paper No. DETC2016-60125, pp. V02AT03A004; 12 pages
doi:10.1115/DETC2016-60125
From:
  • ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 2A: 42nd Design Automation Conference
  • Charlotte, North Carolina, USA, August 21–24, 2016
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5010-7
  • Copyright © 2016 by ASME

abstract

Design spaces that consist of millions or billions of design combinations pose a challenge to current methods for identifying optimal solutions. Complex analyses can also lead to lengthy computation times that further challenge the effectiveness of an algorithm in terms of solution quality and run-time. This work explores combining the design space exploration approach of a Multi-Objective Genetic Algorithm with different instance-based, statistical, rule-based and ensemble classifiers to reduce the number of unnecessary function evaluations associated with poorly performing designs. Results indicate that introducing a classifier to identify child designs that are likely to push the Pareto frontier toward an optima reduce the number of function calculations by 75–85%, depending on the classifier implemented.

Copyright © 2016 by ASME

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