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Eddy current testing (ECT) is a nondestructive testing method for the detection of flaws that uses electromagnetic induction to find defects in conductive materials. In this method, eddy currents are generated in a conductive material by a changing magnetic field. A defect is detected when there is a disruption in the flow of the eddy current. The purpose of this paper is to develop a new noniterative inversion methodology for detecting degradation (defect characterization) such as cracking, corrosion and erosion from the measurement of the impedance variations.

The methodology is based on multi-output support vector machines (SVM) combined with the adaptive database schema design method (SDM). The forward problem was solved numerically using finite element method (FEM), with its accuracy experimentally verified. The multi-output SVM is a statistical learning method that has good generalization capability and learning performance. FEM is used to create the adaptive database required to train the multi-output SVM and the genetic algorithm is used to tune the parameters of multi-output SVM model.

The results show the applicability of multi-output SVM to solve eddy current inverse problems instead of using traditional iterative inversion methods which can be very time-consuming. With the experimental results the authors demonstrate the accuracy which can be provided by the multi-output SVM technique.

The work allows extending the capability of the experimentation ECT defect characterization system developed at LGEP.

A new inversion method is developed and applied to ECT defect characterization. This new concept introduces multi-output SVM in the context of ECT. The real data together with estimated one obtained by multi-output SVM model are compared in order to evaluate the effectiveness of the developed technique.

This paper aims to deal with the development of a newly improved version of teaching learning based optimization (TLBO) algorithm.

Random local search part was added to the classic optimization process with TLBO. The new version is called TLBO algorithm with random local search (TLBO-RLS).

At first step and to validate the effectiveness of the new proposed version of the TLBO algorithm, it was applied to a set of two standard benchmark problems. After, it was used jointly with two-dimensional non-linear finite element method to solve the TEAM workshop problem 25, where the results were compared with those resulting from classical TLBO, bat algorithm, hybrid TLBO, Nelder–Mead simplex method and other referenced work.

New TLBO-RLS proposed algorithm contains a part of random local search, which allows good exploitation of the solution space. Therefore, TLBO-RLS provides better solution quality than classic TLBO.

The principle of microwave characterization of dielectric materials using open-ended coaxial line probe is to link the dielectric properties of the sample under test to the measurements of the probe admittance (Y(f) = G(f)+ jB(f )). The purpose of this paper is to develop an alternative inversion tool able to predict the evolution of the complex permittivity (ε = ε′ – jε″) on a broad band frequency (f from 1 MHz to 1.8 GHz).

The inverse problem is solved using adaptive network based fuzzy inference system (ANFIS) which needs the creation of a database for its learning. Unfortunately, train ANFIS using f, G and B as inputs has given unsatisfying results. Therefore, an inputs selection procedure is used to select the three optimal inputs from new inputs, created mathematically from original ones, using the Jang method.

Inversion results of measurements give, after training, in real time the complex permittivity of solid and liquid samples with a very good accuracy which prove the applicability of ANFIS to solve inverse problems in microwave characterization.

The originality of this paper consists on the use of ANFIS with input selection procedure based on the Jang method to solve the inverse problem where the three optimal inputs are selected from 26 new inputs created mathematically from original ones (f, G and B).

The dielectric properties of materials (complex permittivity) can be deduced from the admittance measured at the discontinuity plane of a coaxial open‐ended probe. This implies the implementation of an inversion procedure. The purpose of this paper is to develop a new non‐iterative inversion methodology in the field of microwave characterization allowing reducing the computation cost comparatively to iterative procedures.

The inversion methodology combines the support vector machine (SVM) technique and the finite element method (FEM). The SVM are used as inverse models. They show good approximation and generalization capabilities. FEM allows the generation of the data sets required by the SVM parameter adjustment. A data set is constituted of input (complex admittance and frequency) and output (complex permittivity) pairs.

The results show the applicability of SVM to solve microwave inverse problems instead of using traditional iterative inversion methods which can be very time‐consuming. The experimental results demonstrate the accuracy which can be provided by the SVM technique.

The paper allows extending the capability of microwave characterization cells developed at Laboratoire de Génie Électrique de Paris.

A new inversion method is developed and applied to microwave characterization. This new concept introduces SVM in the context of microwave characterization. SVM results and iterative inversion procedure results are compared in order to evaluate the effectiveness of the developed technique.

The purpose of this paper is to present a constructive algorithm to design multilayer perceptron neural networks used as approximation models of electromagnetic devices.

The proposed procedure allows automatic determination of both the number of neurons and the synaptic weights of networks with a single hidden layer. The approximation model is used in design optimization problems. The inputs of the neural network correspond to the design parameters whereas the output corresponds to the objective function of the optimization problem. The neural model is then inverted in order to determine which input is associated to a prefixed output.

The performance of the algorithm has been tested on analytical function and on the TEAM workshop problem 25.

As the reliability of the optimum solution is strongly affected by the accuracy of the neural approximation model, the approximation error is kept as low as possible, especially in the maximum/minimum points.

This study aims to improve the bat algorithm (BA) performance for solving optimization problems in electrical engineering.

For this task, two strategies were investigated. The first one is based on including the crossover technique into classical BA, in the same manner as in the genetic algorithm method. Therefore, the newly generated version of BA is called the crossover–bat algorithm (C-BA). In the second strategy, a hybridization of the BA with the Nelder–Mead (NM) simplex method was performed; it gives the NM-BA algorithm.

First, the proposed strategies were applied to solve a set of two standard benchmark problems; then, they were applied to solve the TEAM workshop problem 25, where an electromagnetic field was computed by use of the 2D non-linear finite element method. Both optimization algorithms and finite element computation tool were implemented under MATLAB.

The two proposed optimization strategies, C-BA and NM-BA, have allowed good improvements of classical BA, generally known for its poor solution quality and slow convergence rate.

This study aims to focus on the application of the stochastic algorithms for optimal design of electrical machines. Among them, the authors are interested in particle swarm optimization and teaching–learning-based optimization.

The optimization process is realized by the coupling of the above methods to finite element analysis of the electromagnetic field.

To improve the performance of these algorithms and reduce their computation time, a coupling with the artificial neuron network has been realized.

The proposed strategy is applied to solve two optimization problems: Team workshop problem 25 and switched reluctance motor with flux barriers.