Search results

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 presents a methodology based on Multiobjective Genetic Algorithms (MOGAs) for the design of electrical engineering systems. MOGAs allow one to optimize multiple heterogeneous criteria in complex systems, but also simplify couplings and sensitivity analysis by determining the evolution of design variables along the Pareto‐optimal front.

To illustrate the use of MOGAs in electrical engineering, the optimal design of an electromechanical system has been investigated. A rather simplified case study dealing with the optimal dimensioning of an inverter – permanent magnet motor – reducer – load association is carried out to demonstrate the interest of the approach. The purpose is to simultaneously minimize two objectives: the global losses and the mass of the system. The system model is described by analytical model and we use the MOGA called NSGA‐II.

From the extraction of Pareto‐optimal solutions, MOGAs facilitate the investigation of parametric sensitivity and the analysis of couplings in the system. Through a simple but typical academic problem dealing with the optimal dimensioning of a inverter – permanent magnet motor – reducer – load association, it has been shown that this multiobjective a posteriori approach could offer interesting outlooks in the global optimization and design of complex heterogeneous systems. The final choice between all Pareto‐optimal configurations can be a posteriori done in relation to other issues which have not been considered in the optimization process. In this paper, we illustrate this point by considering the cogging torque for the final decision.

We have proposed an original quantitative methodology based on correlation coefficients to characterize the system interactions.

The effectiveness of full passive wind turbine (WT) systems has been recently demonstrated. Such low cost and reliable structures without active control and with a minimum number of sensors can be efficient only if the system design parameters are mutually adapted through an integrated optimal design approach. The purpose of this paper is to analyze the effectiveness of a passive WT with regard to the variations of PMSG electrical parameters.

This work is more specifically devoted to the sensitivity analysis of a passive WT system according to the electrical variable variations of a Permanent Magnet Synchronous Generator (PMSG). It also investigates the interest of a robust design approach for reducing the sensitivity of the WT efficiency to the most influencing variables.

It is shown that efficiency of the passive WT system is rather sensitive to the variation of the stator flux and DC voltage at the system output.

A robust design approach is investigated in order to reduce the passive WT sensitivity to the stator flux and DC voltage variations.

A wide number of applications requires classifying or grouping data into a set of categories or clusters. The most popular clustering techniques to achieve this objective are K‐means clustering and hierarchical clustering. However, both of these methods necessitate the a priori setting of the cluster number. The purpose of this paper is to present a clustering method based on the use of a niching genetic algorithm to overcome this problem.

The proposed approach aims at finding the best compromise between the inter‐cluster distance maximization and the intra‐cluster distance minimization through the silhouette index optimization. It is capable of investigating in parallel multiple cluster configurations without requiring any assumption about the cluster number.

The effectiveness of the proposed approach is demonstrated on 2D benchmarks with non‐overlapping and overlapping clusters.

The proposed approach is also applied to the clustering analysis of railway driving profiles in the context of hybrid supply design. Such a method can help designers to identify different system configurations in compliance with the corresponding clusters: it may guide suppliers towards “market segmentation”, not only fulfilling economic constraints but also technical design objectives.

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.

The purpose of this study is to investigate and compare the ability of a new optimization technique based on the emulation of the immune system to detect the global maximum with multimodal functions and to test the capability of exploring the parameter space with respect to clustering enhanced Genetic Algorithms (GA).

Both algorithms have been tested on analytical test functions and on numerical functions of applicative interest. A set of performance criteria has been defined in order to numerically compare the performances of both optimization strategies.

Results show the great ability of Artificial Immune Systems (AIS) in thoroughly exploring the space of variables. On the other side, GA are faster to converge to the global optimum, but selection pressure can reduce the number of detected local optima.

This work is an attempt to assess the performances of a relatively new optimization algorithm based on AIS and to find its behavior on multimodal test functions, using GAs as reference optimization technique.

To analyze effectively magnetic shielding effects by shields with fine structure.

Simplification of the fine structure makes it possible to analyze them efficiently. The authors have introduced a homogenization method to estimate effective permeability of magnetic composite structure for the static field. The homogenization method is applied to the analysis of magnetic shields composed of steel plates and steel rods against DC power lines to test its feasibility.

The properties of the magnetic shielding are analyzed by using the homogenization method. The errors of the magnetic fields increase in case of very few layers.

The simplification of the magnetic shields with fine structure by using the homogenization method makes it possible to analyze efficiently magnetic shielding effects, although the accuracy becomes worse in case of very few layers.

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 purpose of this paper is to propose an improved charge simulation method (CSM) based on the real‐coded genetic algorithm (GA), in which the tedious testing and adjusting work in the traditional CSM is avoided. In addition, less simulation charges are used in the simulation model for the same object compared with the traditional CSM.

In the improved CSM, the information of testing points are combined with the matching points, hence the size and locations of simulation charges can be only computed by the information of matching points, and the testing calculation in the traditional CSM is avoided. According to the Maxwell equations, an overdetermined equation is reformulated, and an improved GA is used to solve it. The process to create the initial population is improved, boundary condition information of part matching points and human experience are added into the initial population, which enhanced the convergence rate of the search calculation.

Combining the information of testing points into that of the matching points in the improved CSM, the tedious testing calculation can be avoided, and the GA is effective to solve the overdetermined equation for the improved CSM. By importing the information of part matching points and human experience, the search process can be accelerated and the convergence of the method is ensured. Comparison between the efficiency of traditional CSM and the improved CSM is presented, less simulation charges are used compared with the traditional CSM for the same object.

The improved CSM cannot adjust the count of simulation charges in the equivalent model automatically.

Calculations of a sphere‐plane electrode model and a 64 kV insulator string are carried out to verify the validity of the improved CSM. In addition, it can also be used to compute the power frequency electric field of HV transmission apparatuses.

By improving the process of the traditional CSM and importing the GA, an improved CSM for the calculation of power frequency electric fields produced by high‐voltage apparatuses is presented in the paper, which can avoid the tedious testing and adjusting work in the traditional CSM.

The purpose of this paper is to review the current state of proceedings in the research area of automatic swarm design and discusses possible solutions to advance swarm robotics research.

First, this paper begins by reviewing the current state of proceedings in the field of automatic swarm design to provide a basic understanding of the field. This should lead to the identification of which issues need to be resolved in order to move forward swarm robotics research. Then, some possible solutions to the challenges are discussed to identify future directions and how the proposed idea of incorporating learning mechanism could benefit swarm robotics design. Lastly, a novel evolutionary-learning framework for swarms based on epigenetic function is proposed with a discussion of its merits and suggestions for future research directions.

The discussion shows that main challenge which is needed to be resolved is the presence of dynamic environment which is mainly caused by agent-to-agent and agent-to-environment interactions. A possible solution to tackle the challenge is by incorporating learning capability to the swarm to tackle dynamic environment.

This paper gives a new perspective on how to improve automatic swarm design in order to move forward swarm robotics research. Along with the discussion, this paper also proposes a novel framework to incorporate learning mechanism into evolutionary swarm using epigenetic function.