The University of Southampton
University of Southampton Institutional Repository

Optimizing the Jiles-Atherton Model of Hysteresis by a genetic algorithm

Wilson, Peter R., Ross, J. Neil and Brown, Andrew D. (2001) Optimizing the Jiles-Atherton Model of Hysteresis by a genetic algorithm IEEE Transactions on Magnetics, 37, (2), pp. 989-93. (doi:10.1109/20.917182).

Record type: Article


Modeling magnetic components for simulation in electric circuits requires an accurate model of the hysteresis loop of the core material used. It is important that the parameters extracted for the hysteresis model be optimized across the range of operating conditions that may occur in circuit simulation. This paper shows how to extract optimal parameters for the Jiles-Atherton model of hysteresis by the genetic algorithm approach. It compares performance with the well-known simulated annealing method and demonstrates that improved results may be obtained with the genetic algorithm. It also shows that a combination of the genetic algorithm and the simulated annealing method can provide an even more accurate solution that either method on its own. A statistical analysis shows that the optimization obtained by the genetic algorithm is better on average, not just on a one-off test basis. The paper introduces and applies the concept of simultaneous optimization for major and minor hysteresis loops to ensure accurate model optimization over a wide variety of operating conditions. It proposes a modification to the Jiles-Atherton model to allow improved accuracy in the modeling of the major loop.

PDF ga.pdf - Other
Download (90kB)

More information

Published date: March 2001
Organisations: EEE


Local EPrints ID: 255900
ISSN: 0018-9464
PURE UUID: 2970cec3-80cb-415b-8256-5d9f251dc9b2

Catalogue record

Date deposited: 31 May 2001
Last modified: 18 Jul 2017 09:50

Export record



Author: Peter R. Wilson
Author: J. Neil Ross
Author: Andrew D. Brown

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton:

ePrints Soton supports OAI 2.0 with a base URL of

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.