• Feb 07, 2023 News!IJMO will adopt Article-by-Article Work Flow   [Click]
  • Aug 25, 2023 News!Vol. 13, No. 3 has been published with online version.   [Click]
  • Dec 21, 2023 News!Vol. 13, No. 4 has been published with online version.   [Click]
General Information
Editor-in-chief
Prof. Adrian Olaru
University Politehnica of Bucharest, Romania
I'm happy to take on the position of editor in chief of IJMO. It's a journal that shows promise of becoming a recognized journal in the area of modelling and optimization. I'll work together with the editors to help it progress.
IJMO 2019 Vol.9(4): 230-233 ISSN: 2010-3697
DOI: 10.7763/IJMO.2019.V9.714

Enhancing Simulation-Driven Optimization by Machine-Learning

Yoel Tenne

Abstract—Computer simulations are being extensively used in engineering design optimization to evaluate candidate designs instead of real-world experiments. Often for some of the candidate designs the simulation will fail for an unknown reason, which leads to wasted computer resources and poor final results. To handle this challenge both effectively and efficiently this study presents an implementation in which classifiers, borrowed from the domain of machine learning, are integrated into the optimization process to predict if a candidate design is valid or not prior to evaluating it with the simulation. To further enhance the optimization effectiveness two different search methods are used. Numerical experiments show the merit of the proposed implementation.

Index Terms—Expensive black-box functions, machine learning, classifiers, metamodels.

Yoel Tenne is with School of Mechanical Engineering, Ariel University, Israel (e-mail: y.tenne@ariel.ac.il).

[PDF]

Cite: Yoel Tenne, "Enhancing Simulation-Driven Optimization by Machine-Learning," International Journal of Modeling and Optimization vol. 9, no. 4, pp. 230-233, 2019.

Copyright © 2008-2024. International Journal of Modeling and Optimization. All rights reserved.
E-mail: ijmo@iacsitp.com