Francesco Riganti Fulginei

Softcomputing for the identification of the Jiles-Atherton model parameters

This paper presents a Jiles-Atherton hysteresis model identification method based on a partnership of heuristic techniques and fuzzy logic (softcomputing). Two different softcomputing approaches are proposed and analyzed: a partnership between genetic algorithms (GAs) and fuzzy logic (FL) and one between GAs and simulated annealing (SA). Validations of both symmetric (saturated or minor loops) and asymmetric loops are described.

Genetic algorithms and neural networks generalizing the Jiles-Atherton model of static hysteresis for dynamic loops

This paper presents a method based on genetic algorithms and neural networks suitable for finding the five parameters of the Jiles-Atherton (JA) model for generalization to dynamic hysteresis loops. The aim is to obtain an equivalent static model for dynamic loops by updating its parameters varying the frequency of the imposed magnetic field H(t). Validations of the present approach compared to other numerical approaches, based on adding frequency-dependent losses to the static model, and versus experimental tests will be shown.

Neural Network Approach for Modelling Hysteretic Magnetic Materials Under Distorted Excitations

A Neural Network (NN) approach for modelling dynamic hysteresis is presented. The modelling of the dynamic behavior of hysteretic materials and devices must take into account magnetodynamic effects. In the present paper these tasks are simultaneously modelled by means of an ad-hoc Neural System (NS) based on an array of 3-input 1-output Feed Forward NNs. Each NN is dedicated to a particular typology of the excitation field (prediction of flux density from a known waveform of the magnetic field strength or vice-versa) and it manages just a fixed portion of the dynamic hysteresis loop. The whole hysteretic path is reconstructed by the union of the evaluations made by different NNs of the NS. The NS is able to perform the simulation of any kind of dynamic loop (saturated and non-saturated, symmetric or asymmetric) generated by any assigned arbitrarily distorted excitations into a fixed range of frequencies. Numerical validations are presented.

Comparative analysis between modern heuristics and hybrid algorithms

Purpose – The purpose of the present paper is to show a comparative analysis of classical and modern heuristics such as genetic algorithms, simulated annealing, particle swarm optimization and bacterial chemotaxis, when they are applied to electrical engineering problems.Design/methodology/approach – Hybrid algorithms (HAs) obtained by a synergy between the previous listed heuristics, with the eventual addiction of the Tabu Search, have also been compared with the single heuristic performances.Findings – Empirically, a different sensitivity for initial values has been observed by changing type of heuristics. The comparative analysis has then been performed for two kind of problems depending on the dimension of the solution space to be inspected. All the proposed comparative analyses are referred to two corresponding different cases: Preisach hysteresis model identification (high dimension solution space) and load‐flow optimization in power systems (low dimension solution space).Originality/value – The ori...

Shape Optimization of Multistage Depressed Collectors by Parallel Evolutionary Algorithm

In this paper a novel parallel meta-heuristic algorithm called MeTEO is presented, applied to the shape optimization of multistage depressed collectors, simulated by means of a Finite Element collector and electron gun simulator, COLLGUN, which uses the Constructive Solid Geometry for the description of the device shape. METEO is a hybrid algorithm composed by three different heuristics: FSO (Flock of Starlings Optimization), PSO (Particle Swarm Optimization), and BCA (Bacterial Chemotaxis Algorithm); it performs the optimization using both the topological and the metric rules and offers a natural parallel implementation that allows speeding up the whole process of optimization by the fitness modification (FM).

Bouc–Wen Hysteresis Model Identification by the Metric-Topological Evolutionary Optimization

From a computational point of view, the identification of Bouc-Wen (BW) hysteresis model is a hard task due to the large number of parameters to be found. This is one of the reasons for which it is rarely used for modeling magnetic hysteresis, where other hysteresis models are widely used (e.g., Preisach and Jiles-Atherton). However, the opportunities that the differential expression of BW model could offer for its use in more complex computation task (e.g., nonlinear inductors inserted into a circuit and so on) justify a deeper investigation on its adoption in ferromagnetism. In this paper, using a new hybrid heuristic called metric-topological-evolutionary optimization (MeTEO), the BW identification is presented. MeTEO is a powerful algorithm based on a synergic and strategic use of three evolutionary heuristics: 1) the flock-of-starlings optimization, which shows not only high exploration capability, but also a lack of convergence; 2) the particle swarm optimization, which has a good convergence capability; and 3) the bacterial chemotaxis algorithm, which has no collective behavior or exploration skill, but has high convergence capability. MeTEO is designed to use parallel architectures and exploits the fitness modification technique. Numerical validations are presented in comparison with the performances obtained using other approaches available in the literature.

Generalization of the static Preisach model for dynamic hysteresis by a genetic approach

A method able to generalize the static Preisach hysteresis model for dynamic loops under sinusoidal time-varying fields is presented in this paper. The aim is to obtain an equivalent static model for dynamic loops. Then, the Preisach distribution function parameters are updated according to the frequency of the excitation magnetic field. The frequency-dependent parameters have been evaluated by genetic algorithms. Validations based on a comparison of the results of the present approach have been made with a different classical numerical approach modeling dynamic loops.

CFSO3: A New Supervised Swarm-Based Optimization Algorithm

We present CFSO3, an optimization heuristic within the class of the swarm intelligence, based on a synergy among three different features of the Continuous Flock-of-Starlings Optimization. One of the main novelties is that this optimizer is no more a classical numerical algorithm since it now can be seen as a continuous dynamic system, which can be treated by using all the mathematical instruments available for managing state equations. In addition, CFSO3 allows passing from stochastic approaches to supervised deterministic ones since the random updating of parameters, a typical feature for numerical swam-based optimization algorithms, is now fully substituted by a supervised strategy: in CFSO3 the tuning of parameters is a priori designed for obtaining both exploration and exploitation. Indeed the exploration, that is, the escaping from a local minimum, as well as the convergence and the refinement to a solution can be designed simply by managing the eigenvalues of the CFSO state equations. Virtually in CFSO3, just the initial values of positions and velocities of the swarm members have to be randomly assigned. Both standard and parallel versions of CFSO3 together with validations on classical benchmarks are presented.

Swarm/flock optimization algorithms as continuous dynamic systems

A new general typology of optimization algorithms, inspired to classical swarm intelligence, is presented. They are obtained by translating the numerical swarm/flock-based algorithms into differential equations in the time domain and employing analytical closed-forms written in the continuum. The use of circulant matrices for the representation of the connections among elements of the flock allowed us to analytically integrate the differential equations by means of a time-windowing approach. The result of this integration provides functions of time that are closed-forms, suitable for describing the trajectories of the flock members: they are directly used to update the position and the velocity of each bird/particle at each step (time window) and consequently they substitute in the continuous algorithm the classical updating rules of the numerical algorithms. Thanks to the closed forms it is also possible to analyze the effects due to the tuning of parameters in terms of exploration or exploitation capabilities. In this way we are able to govern the behavior of the continuous algorithm by means of non stochastic tuning of parameters. The proposed continuous algorithms have been validated on famous benchmark functions, comparing the obtained results with the ones coming from the corresponding numerical algorithms.

A neuro-genetic and time-frequency approach to macromodeling dynamic hysteresis in the harmonic regime

A numerical approach for the evaluation of hysteresis loops in the harmonic regime is presented. Genetic algorithms (GAs) are used to train neural networks (NNs) with the aim of generalizing the Jiles-Atherton (JA) static hysteresis model for dynamic loops. The NN training is based on symmetrical and asymmetrical, major and minor loops under sinusoidal excitation with or without offset. Subsequently, the harmonic magnetic time period has been partitioned into suitable time windows into which the field has been fitted by sinusoids with offset. New JA parameters, estimated by the trained NNs in each partitioning time window, have been inserted into the JA static model to calculate the magnetization waveform, time window by time window. Validations are shown.