Hysteresis parameters estimation using a modified harmony search

• Published in: Compel 2013-01, Vol.32 (6), p.1974-1985
• Main Authors: dos Santos Coelho, Leandro, Cocco Mariani, Viviana, de Athayde Costa e Silva, Marsil, Jhoe Batistela, Nelson, Vianei Leite, Jean
• Format: Article
• Language: English
• Subjects: Algorithms; Behavior; Chaos theory; Electrical & electronic engineering; Energy consumption; Engineering; Evolution; Genetic algorithms; Harmony (Music); Hysteresis; Improvisation; Magnetic fields; Mathematical analysis; Mathematical models; Musicians & conductors; Optimization techniques; Parameter estimation; Parameter identification; Searching; Studies; Variables; Vectors (mathematics)
• ISSN: 0332-1649; 2054-5606
• DOI: 10.1108/COMPEL-03-2012-0033

Purpose – The purpose of this paper is to introduce a chaotic harmony search (CHS) approach based on the chaotic Zaslavskii map to parameters identification of Jiles-Atherton vector hysteresis model. Design/methodology/approach – In laminated magnetic cores when the magnetic flux rotates in the lamination plane, one observes an increase in the magnetic losses. The magnetization in these regions is very complex needing a vector model to analyze and predict its behavior. The vector Jiles-Atherton hysteresis model can be employed in rotational flux modeling. The vector Jiles-Atherton model needs a set of five parameters for each space direction taken into account. In this context, a significant amount of research has already been undertaken to investigate the application of metaheuristics in solving difficult engineering optimization problems. Harmony search (HS) is a derivative-free real parameter optimization metaheuristic algorithm, and it draws inspiration from the musical improvisation process of searching for a perfect state of harmony. In this paper, a CHS approach based on the chaotic Zaslavskii map is proposed and evaluated. Findings – The proposed CHS presents an efficient strategy to improve the search performance in preventing premature convergence to local minima when compared with the classical HS algorithm. Numerical comparisons with results using classical HS, genetic algorithms (GAs), particle swarm optimization (PSO), and evolution strategies (ES) demonstrated that the performance of the CHS is promising in parameters identification of Jiles-Atherton vector hysteresis model. Originality/value – This paper presents an efficient CHS approach applied to parameters identification of Jiles-Atherton vector hysteresis model.