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The purpose of this paper is to describe an optimization methodology based on a mixed (analytical‐numerical) design model and evolutionary algorithms.

In this paper, optimal sizing is used to design the new architecture of an embedded electrical system. An approach is presented for defining the specifications and a method for modeling an embedded electrical network. This will enable the optimization problem to be set.

This paper shows that the proposed methodology is useful for sizing an embedded electrical system. It is illustrated by its application to the sizing of an aircraft electrical power channel (auto‐transformer‐rectifier unit type).

An original approach is proposed for limiting the search space before using the evolutionary algorithms. The advantage of this approach is to increase convergence speed of the evolutionary algorithms.

This paper deals with the collaborative design of electromagnetic devices over the internet network. The design is made by both mechanical and electrical engineers. So, the paper tries to show the importance but also constraints to size such a system using a collaborative optimisation process.

The paper compares two approaches in order to size an electromechanical actuator between mechanical and electrical engineers. In the first one, each profession designs its part, and only common constrained are negotiated. This can result in a design process with many iterations. In the second one, electrical and mechanical engineers built together a common model of the structure and a common list of specifications: this allows a global optimisation that is more efficient.

The main result of the paper is that the second approach in which a global model is built between electrical and mechanical engineers is more efficient.

The originality of the paper is to explore the problems and difficulties of an optimisation of an electromechanical device between engineers of different culture working together over the internet network.

This paper is a synthesis paper which seeks to discuss an optimisation framework using software components, which is a new emerging paradigm in computer science.

The goal of this paper is to show the efficiency of the software component approach for the implementation of optimisation frameworks for engineering systems in general, and electromagnetic systems in particular.

This paper highlights the component standard, a generator based on analytical expressions of the system, and an optimization service. References and examples show application in the area of electromagnetic components and systems.

This paper presents CADES, a framework dedicated to system design, based on optimization needs. The framework is defined with a standard implementing the software component paradigm and a pattern to use it. Indeed, this pattern details how to create and use a component (the model of the device to design).

This paper shows how the new emerging paradigm of software components can be used for building new generations of optimisation environment allowing capitalisation and reuse by combination of software components containing models and optimisation algorithms.

The purpose of this paper is to illustrate automatic differentiation (AD) as a new technology for the device sizing in electromagnetism by using gradient constrained optimization. Component architecture for the design of engineering systems (CADES) framework, previously described, is presented here with extended features.

The paper is subject to further usage for optimization of AD (also named algorithmic differentiation) which is a powerful technique that computes derivatives of functions described as computer programs in a programming language like C/C++, FORTRAN.

Indeed, analytical modeling is well suited regarding optimization procedure, but the modeling of complex devices needs sometimes numerical formulations. This paper then reviews the concepts implemented in CADES which aim to manage the interactions of analytical and numerical modeling inside of gradient‐based optimization procedure. Finally, the paper shows that AD has no limit for the input program complexity, or gradients accuracy, in the context of constrained optimization of an electromagnetic actuator.

AD is employed for a large and complex numerical code computing multidimensional integrals of functions. Thus, the paper intends to prove the AD capabilities in the context of electromagnetic device sizing by means of gradient optimization. The code complexity as also as the implications of AD usage may stand as a good reference for the researchers in this field area.

The purpose of this paper is to make easily accessible models to test and compare the optimization algorithms we develop.

For this, the paper proposes an optimization framework based on software component, web service, and plugin to exploit these models in different environments.

The paper illustrates the discussion with optimizations in Matlab™ and R (www.r-project.org) of a transformer described and exploitable from the internet.

The originality is to make easy implementation of simulation model and optimization algorithm coupling using software component, web service, and plugin.

The purpose of this paper is to investigate the impact of different mathematical formulations of the problem of optimal design of electrical machines on the results obtained using a local optimization solver. The aim is to investigate the efficiency and reliability of standard local solvers when handling different mathematical formulations. This could provide guidelines for designers in practical engineering applications.

The paper proposes six equivalent mathematical formulations of the optimal design problem of a slotless permanent‐magnet electric rotating machine. The authors investigate the impact of these different mathematical formulations on the results obtained using a local optimization solver which is well‐known in the engineering community: MatLab's fmincon function. The paper first computationally compares the six proposed formulations with a fixed value for the number of pole pairs p, that gives continuous optimization problems, then discusses some results when p is free on three mixed‐integer formulations.

The paper shows that, even though the considered formulations are mathematically equivalent, their numerical performances are different when an optimization solver, such as the one proposed by MatLab in fmincon, is used. Thus, the designer must take care about the formulation of the design problem in order to make more efficient the use of these kind of algorithms.

In the context of engineering applications, one usually resorts to well known and easy to use optimization solvers. The same optimization problem can be often formulated in different ways. Furthermore, the formal description of optimization problems has an impact on the applicability and efficiency of the corresponding solution methods. This is usually not taken into account when optimization solvers are exploited. The originality of this paper is in building on the theory of reformulations in mathematical optimization to investigate and highlight the impact of formulation differences.

The frequency simulation and optimisation of electromagnetic compatibility (EMC) filter is often computation time consuming. The purpose of this paper is to propose an approach for easy and fast modelling and optimization of power electronics structures.

The paper proposes an approach for easy and fast modelling and optimization of power electronics structures. It focuses on the EMC filter design. To achieve this task time simulation, FFT and automatic frequency modelling are combined.

An automatic frequency modelling is proposed and also gives automatically the model gradients. Therefore, the model can be used to optimize the EMC filter, but also can help in choosing its topology. Several optimization algorithms are used and compared.

The power electronics load is supposed to be a set of predefined harmonic sources, obtained by time simulation + FFT before the optimisation process.

The frequency model allows for the rapid designing and comparing of several structures or modelling hypothesis with regard to the parasitic elements and circuit imperfections.

The frequency model is automatically generated, and sizing criteria on the component (e.g. inductors, capacitor) can be added in an analytical form, for example, to deal with volume or mass criteria.

This paper aims to describe a modelling and solving methodology of a (static converter–electric motor–control) system for its sizing by optimization, considering the dynamic thermal heating of the machine.

The electrical drive sizing model is composed of two simulators (electrical and thermal) that are co-simulated with a master−slave relationship for the time step management. The computation is stopped according to simulation criteria.

This paper details a methodology to represent and size an electrical drive using a multiphysics and multidynamics approach. The thermal simulator is the master and calls the electrical system simulator at a fixed exchange time step. The two simulators use a dedicated dynamic time solver with adaptive time step and event management. The simulation automatically stops on pre-established criteria, avoiding useless simulations.

This paper aims to present a generic methodology for the sizing by optimization of electrical drives with a multiphysics approach, so the precision and computation time highly depend on the modelling method of each components. A genetic multiobjective optimization algorithm is used.

The methodology can be applied to size electrical drives operating in a thermally limited zone. The power electronics converter and electrical machine can be easily adapted by modifying their sub-model, without impacting the global model and simulation principle.

The approach enables to compute a maximum operating duration before reaching thermal limits and to use it as an optimization constraint. These system considerations allow to over constrain the electrical machine, enabling to size a smaller machine while guaranteeing the same output performances.

Life cycle analysis (LCA) is more and more used in the context of electromagnetic product design. But it is often used to check a design solution regarding environmental impacts after technical and economical choices. This paper aims to investigate life cycle impact optimization (LCIO) and compare it with the classical life cycle cost optimization (LCCO).

First, a model of a dry-type transformer using different materials for windings and the magnetic core is presented. LCCO, which is a mixed continuous-discrete, multi-objective technico-economic optimization, is done using both deterministic and genetic algorithms. LCCO results and optimization performances are analyzed, and an LCA is presented for a set of optimal solutions. The final part is dedicated to LCIO, where the paper shows that these optimal solutions are close to those obtained with LCCO.

This paper investigated LCIO using an environmental impacts model that has been introduced in the optimization framework Component Architecture for the Design of Engineering Systems. The paper shows how a mixed continuous-discrete, multi-objective technico-economic optimization can be done using an efficient deterministic optimization algorithm such as Sequential Quadratic Programming. Thanks to the technico-economic-environmental model and the efficient optimization algorithm, both LCCO and LCIO were performed separately and together. It has been shown that optimal solutions are similar, leading to the conclusion that only one modeling is required (economic or environmental) but on the life cycle.

The classical sequential methodology of design is improved here by the use of a model of calculation of the environmental impacts allowing the optimization. This original optimization allowed the authors to show that an analysis of the life cycle from an economic point of view or from an environmental point of view led to quasi-equivalent technical solutions. The key is to take into account the life cycle of the product.

The purpose of this paper is to present an effective optimization strategy applied in a physical structure optimization of a semiconductor power metal oxide semiconductor field‐effect transistor (MOSFET), with an expensive integration constraint computation.

In order to deal with inaccuracy due to inevitable numerical errors in the objective function calculation (the power losses of the power MOSFET) and in the constraint computation, the paper proposes to use the progressive quadratic response surface method (PQRSM).

The paper focuses on four aspects: the inevitable numerical errors in the power loss and the integration constraint computation; the response surface approximation (RSA) method; the PQRSM principle; and finally the comparisons of several optimization methods applied on this application problem.

An original optimization method, PQRSM, is proposed for reducing the oscillation problem of a semi‐analytical model. The optimization results of PQRSM have been compared with the evolution strategy (ES) algorithm, with similar results but faster computation.