| Issue |
Eur. Phys. J. AP
Volume 12, Number 2, November 2000
|
|
|---|---|---|
| Page(s) | 109 - 115 | |
| DOI | https://doi.org/10.1051/epjap:2000177 | |
| Published online | 15 November 2000 | |
https://doi.org/10.1051/epjap:2000177
Using neural networks to speed up optimization algorithms*
1
The Institute of Computer Science, University of Wrocław,
Przesmyckiego 21, 51-151 Wrocław, Poland
2
CERN, 1211 Geneva 23, Switzerland
Corresponding author: stephan.russenschuck@cern.ch
Received:
11
May
2000
Accepted:
29
August
2000
Published online: 15 November 2000
The paper presents the application of Radial-basis-function (RBF) neural networks to speed up deterministic search algorithms used for the design and optimization of superconducting LHC magnets. The optimization of the iron yoke of the main dipoles requires a number of numerical field computations per trial solution as the field quality depends on the excitation of the magnets. This results in computation times of about 30 minutes for each objective function evaluation (on a DEC-Alpha 600/333) and only the most robust (deterministic) optimization algorithms can be applied. Using a RBF function approximator, the achieved speed-up of the search algorithm is in the order of 25% for problems with two parameters and about 18% for problems with three and five design variables.
PACS: 02.60.Ed – Interpolation; curve fitting / 07.05.Mh – Neural networks, fuzzy logic, artificial intelligence / 02.60.Pn – Numerical optimization
© EDP Sciences, 2000
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