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Global optimization in electrical engineering using stochastic methods requires usually a large amount of CPU time to locate the optimum, if the objective function is calculated either with the finite element method (FEM) or the boundary element method (BEM). One approach to reduce the number of FEM or BEM calls using neural networks and another one using multiquadric functions have been introduced recently. This paper compares the efficiency of both methods, which are applied to a couple of test problems and the results are discussed.

The common approach to continuous and discrete optimisation problems in electromagnetics does not take into account uncertainties and variations of the design variables. Local sensitivity analysis is usually performed only after the optimisation run to study the behaviour of the objective function in the neighbourhood of the optimum. However, this procedure may prove inefficient if the optimum has to be rejected due to sensitivity considerations and a new run has then to be performed. In this paper an alternative approach, which takes into account uncertainties in the design variables and physical data, is presented, and an analytical function is used to highlight the features of the proposed method. The essence of the technique is to couple the optimisation with a series of worst case analyses which are embedded in the optimisation loop. The method is fully general and can be applied to any optimisation method. The additional computational costs associated with the procedure maybe relatively high, but in the authors’ opinion the obtained gains in user confidence in the solution and the computational savings in some cases far offset the possible drawbacks of the method.

The purpose of this paper is to explore the potential of standard quantum-based particle swarm optimization (QPSO) methods for solving electromagnetic inverse problems.

A modified QPSO algorithm is designed.

The modified QPSO algorithm is an efficient and robust global optimizer for optimizing electromagnetic inverse problems. More specially, the experimental results as reported on different case studies demonstrate that the proposed method can find better final optimal solution at an early stage of the iterating process (uses less iterations) as compared to other tested optimal algorithms.

The modifications include the design of a new position updating formula, the introduction of a new mutation strategy and a dynamic control parameter to intensify the convergence speed of the algorithm.

This paper aims to show on a widely used benchmark problem that chaotic sequences can improve the search ability of evolution strategies (ES).

The Lozi map is used to generate new individuals in the framework of ES algorithms. A quasi‐Newton (QN) method is also used within the iterative loop to improve the solution's quality locally.

It is shown that the combined use of chaotic sequences and QN methods can provide high‐quality solutions with small standard deviation on the selected benchmark problem.

Although the benchmark is considered to be representative of typical electromagnetic problems, different test cases may give less satisfactory results.

The proposed approach appears to be an efficient general purpose optimizer for electromagnetic design problems.

This paper introduces the use of chaotic sequences in the area of electromagnetic design optimization.

The use of niching genetic algorithms can provide a method of a more widespread search of the design space for a device than more conventional methods. It provides, in effect, a breadth first rather than a depth first search. Thus several alternative designs may be evaluated in parallel.

Introduces papers from this area of expertise from the ISEF 1999 Proceedings. States the goal herein is one of identifying devices or systems able to provide prescribed performance. Notes that 18 papers from the Symposium are grouped in the area of automated optimal design. Describes the main challenges that condition computational electromagnetism’s future development. Concludes by itemizing the range of applications from small activators to optimization of induction heating systems in this third chapter.

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.

The aim of this paper is to reduce the evaluations number of the fine model within the output space mapping (OSM) technique in order to reduce their computing time.

In this paper, n-level OSM is proposed and expected to be even faster than the conventional OSM. The proposed algorithm takes advantages of the availability of n models of the device to optimize, each of them representing an optimal trade-off between the model error and its computation time. Models with intermediate characteristics between the coarse and fine models are inserted within the proposed algorithm to reduce the number of evaluations of the consuming time model and then the computing time. The advantages of the algorithm are highlighted on the optimization problem of superconducting magnetic energy storage (SMES).

A major computing time gain equals to three is achieved using the n-level OSM algorithm instead of the conventional OSM technique on the optimization problem of SMES.

The originality of this paper is to investigate several models with different granularities within OSM algorithm in order to reduce its computing time without decreasing the performance of the conventional strategy.

The purpose of this paper is to propose a multi-objective optimization algorithm, which can improve both the performance robustness and the constraint feasibility when the uncertainty in design variables is considered.

Multi-objective robust optimization by gradient index combined with the reliability-based design optimization (RBDO).

It is shown that searching for the optimal design of the TEAM problem 22, which can minimize the magnetic stray field by keeping the target system energy (180 MJ) and improve the feasibility of superconductivity constraint (quenching condition), is possible by using the proposed method.

RBDO method applied to the electromagnetic problem cooperated with the design sensitivity analysis by the finite element method.

The purpose of this paper is to present a new method to obtain robust solutions to electromagnetic optimization problems, solved with evolutional algorithms, which are insensitive to changes in design parameters such as spatial size, positioning and material constant.

Adjoint variable method is employed to evaluate the sensitivity of individuals in evolutional processes.

It is shown in the numerical examples, where the present method is applied to optimization of a superconducting energy storage system and C‐shape magnet, that robust solutions are actually obtained which are insensitive to deviations in spatial sizes.

Unlike usual optimization methods, the present method takes into account deviation in the design parameters due to production errors and long‐term changes. Moreover, the present method is limited to about twice the computational cost of non‐robust optimization methods.