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Learning to Set Up Numerical Optimizations of Engineering Designs

Published

Author(s)

Mark Schwabacher, T Ellman, H Hirsh

Abstract

Gradient-based numerical optimization of complex engineering designs offers the promise of rapidly producing better designs. However, such methods generally assume that the objective function and constraint functions are continuous, smooth, and defined everywhere. Unfortunately, realistic simulators tend to violate these assumptions, making optimization unreliable. Several decisions that need to be made in setting up an optimization, such as the choice of a starting prototype, and the choice of a formulation of the search space, can make a difference in the reliability of the optimization. Machine learning can improve gradientbased methods by making these choices based on the results of previous optimizations. This article demonstrates this idea by using machine learning for four parts of the optimization setup problem: selecting a starting prototype from a database of prototypes, synthesizing a new starting prototype, predicting which design goals are achievable, and selecting a for- mulation of the search space. We use standard tree-induction algorithms (C4.5 and CART). We present results in two realistic engineering domains: racing yachts, and supersonic aircraft. Our experimental results show that using inductive learning to make setup decisions improves both the speed and the reliability of design optimization.
Citation
Artificial Intelligence in Engineering
Volume
12:2

Keywords

Case-Based Reasoning, Decision Tree Induction, Engineering Design, Numerical Optimization, Reformulation

Citation

Schwabacher, M. , Ellman, T. and Hirsh, H. (1998), Learning to Set Up Numerical Optimizations of Engineering Designs, Artificial Intelligence in Engineering, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=822344 (Accessed June 22, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created January 1, 1998, Updated February 19, 2017