Search

This paper presents an effective design method for inductors which is based on the multi-objective optimization accelerated by the artificial neural network (ANN). In the learning phase prior to the optimization phase, ANN is trained for 1000 input–output data sets obtained from the finite-element analysis for randomly generated dimensional parameters. The magnetic hysteresis of the ferrite core is modeled by the play model to evaluate the hysteresis losses. The multi-objective optimization problems are solved by the genetic algorithm in which the magnetic loss is effectively computed by the trained ANN to reduce the core volume as well as magnetic loss. The Pareto solutions for an EI-shaped ferrite core are obtained for different inductances. It is shown that the proposed method works much faster than the conventional optimization, and the magnetic loss and the inductance of the optimized inductor agree well with the experimental results. [ABSTRACT FROM AUTHOR]

This paper presents a new topology optimization method based on basis functions for design of rotating machines. In this method, the core shape of a given rotating machine is represented by the linear combination of the basis functions. The shape is then freely deformed by changing the weighting coefficients to the basis functions to find the optimal shape. The proposed method is compared with the conventional shape optimization based on polygon morphing. The former is shown to outperform the latter in both interior permanent magnet and synchronous motor models. [ABSTRACT FROM AUTHOR]

This paper presents a topology optimization method for multimaterial models based on the normalized Gaussian network. In this method, one can determine optimal shapes of machines that are composed of various materials, such as iron, magnetic, and non-magnetic material. The present method is applied to the optimization of the average torque for interior permanent magnet motor to determine the distributions of magnetic core and flux barrier as well as magnets. The optimization results show that average torque can be improved using as small amount of magnet as possible. In addition, the characteristic of the present method is discussed in detail. [ABSTRACT FROM PUBLISHER]

This paper presents the topology optimization of a synchronous reluctance motor using the normalized Gaussian network. In the optimization, the average torque and iron loss are considered. In the resultant motor, the area of the rotor surface adjacent to the stator is found to be reduced when the weight for the iron loss is sufficiently large. On the other hand, large flux barriers are present in the rotor when the average torque is maximized without considering the iron loss. [ABSTRACT FROM PUBLISHER]

This paper presents parameter and topology optimizations of wideband antennas for microwave energy harvesters based on finite difference time-domain computations. The antenna shapes are optimized to reduce return losses in a specific frequency band using micro-genetic algorithm. The shape parameters of a two-arm planar spiral antenna are optimized. In addition, the topology optimization is performed using normalized Gaussian network, where spatial symmetries are assumed. It is shown that the return losses of the optimized antennas are <−10 dB from 1 to 2 GHz in which they have isotropic directivity. [ABSTRACT FROM PUBLISHER]

This paper presents robust topology optimization of electromagnetic machines by considering the magnetic degradation caused by mechanical and thermal stresses in punching, shrinking fitting, and other manufacturing processes. The topology optimization is performed using two methods: one is the robust genetic algorithm in which random noises are added to the magnetic characteristic parameters and the other takes the deviations in the objective and constraint functions due to the degradation into account. These methods are applied to optimization of the flux barrier shapes in an interior permanent magnetic motor to find that one can successfully realize robust design. [ABSTRACT FROM PUBLISHER]

This paper proposes a two-step method for topology optimization, in which after global search is performed by the on–off method, and then local search is carried out by the level set approach. The genetic and gradient-based algorithms are employed for the former and latter optimizations, respectively. This present method is applied to two numerical examples; topology optimizations of a magnetic shield system and interior permanent magnetic motor. It is shown that this present method can find solutions with better performances in comparison with the conventional on–off method. [ABSTRACT FROM AUTHOR]

Usually, multiple objectives have to be considered when an electromagnetic apparatus is optimally designed. In such a multipurposed optimal design problem many solutions exist and are termed Pareto optimum. Hence, we have previously proposed a multipurposed optimal design method based on a game theory. The proposed method can choose one rational solution from Pareto optima. It is, however, unconfirmed what the signification of the chosen rational solution is. Therefore, the signification is investigated by calculating the weight parameters of a weighted summed objective function. [ABSTRACT FROM AUTHOR]

This paper presents the optimization of a meander line antenna finite-element model by means of an adaptive genetic algorithm (GA). To search for optimal antenna configurations, the present method employs a GA with an adaptive method that adjusts the characteristics of its selection, crossover, and mutation operators in order to maintain a diverse set of high-quality candidate solutions during its execution. It is shown that the present method can find an optimal solution faster than the conventional GA. Moreover, the fitness values of the optimal solutions obtained by the present method are better than those obtained by conventional GA. [ABSTRACT FROM AUTHOR]

This paper presents multi-objective optimization of meander line antennas (MLA) for passive UHF-band Radio Frequency Identification. The characteristics of the MLA loaded by Cockcroft-Walton (CW) circuit are analyzed by hybrid analysis of the finite difference time domain method and modified nodal analysis. The antenna shape is optimized in order to maximize the output voltage of the CW circuit and minimize the antenna size. [ABSTRACT FROM AUTHOR]

In parameter optimization using the finite element method (FEM), re-meshing with long computational time must be performed many times. Although voxel-based FEM is one of the effective techniques to avoid the re-meshing, it has less accuracy in comparison with the conventional FEM because of stair-shaped boundaries. In this work, to improve the computational accuracy in the voxel-based FEM, a new homogenization method is presented. In the present method, the effective reluctivity of finite elements at material interfaces is determined on the basis of magnetic circuit. The present method is successfully applied to three dimensional shape optimization of an inductor model. [ABSTRACT FROM AUTHOR]

This paper reports novel design method of waveguide slot array antenna with dielectric lenses by using the evolutionary optimization. We adopt the micro-genetic algorithm (MGA) with the real-coded gene as the optimization method and the finite-difference time-domain (FDTD) method to evaluate the objective function. The purpose of this study is to establish the design method of the above antenna by optimizing slot length and offset parameters. In the optimization of the antenna aperture distribution, it is shown that the optimized aperture field becomes sufficiently uniform, and the side-lobe levels (SLL) are improved in comparison with those of conventional antennas. Another optimization in which the objective function is evaluated by the array factor controls directly the side-lobe levels, to design low side-lobe antennas. By using the proposed method, the target antenna is optimized to satisfy the given SLL. It is confirmed that the proposed design method is a promising tool for designing the antennas loaded with dielectric materials. [ABSTRACT FROM PUBLISHER]

This paper presents evolutional design of antennas for UHF-band passive IC tags. The wire-antenna shapes are optimized using the genetic algorithm (GA) aiming at maximization of feeding power to the IC tag as well as minimization of the antenna size. The characteristics of the antennas printed on a dielectric substrate are evaluated using the moment method in the optimization processes. The genotypes for GA are introduced to express the shapes of lattice antennas, which include loops and floating lines, and spiral antennas. The present genotypes for the latter are also valid for optimization of spiral structure of, for example, inductors and filters. It is shown that optimized lattice and spiral antennas have smaller sizes in comparison with the conventional meander antennas. [ABSTRACT FROM PUBLISHER]

This paper presents parameter and topology optimization of inductor shapes using evolutionary algorithms. The goal of the optimization is to reduce the size of inductors satisfying the specifications on inductance values under weak and strong bias-current conditions. The inductance values are computed from the finite-element (FE) method taking magnetic saturation into account. The result of the parameter optimization, which leads to significant reduction in the volume, is realized for test, and the dependence of inductance on bias currents is experimentally measured, which is shown to agree well with the computed values. Moreover, novel methods are introduced for topology optimization to obtain inductor shapes with homogeneous ferrite cores suitable for mass production. [ABSTRACT FROM AUTHOR]

In this paper, we introduce an approach for the design of electromagnetic devices based on the use of Estimation of Distribution Algorithms (EDAs), coupled with approximation-based local search around the most promising solutions. The main idea is to combine the power of EDAs in the solution of hard optimization problems with the faster convergence provided by the local search using local approximations. The resulting hybrid algorithm is tested on a numerical benchmark problem. [ABSTRACT FROM AUTHOR]

Although probabilistic optimization methods based on genetic algorithm (GA) provides accurate results, its performance is sometimes considerably sensitive to parameter changes. Moreover, the constraints are violated due to such parameter changes. A robust GA which performs random perturbation during optimization processes has been applied to some mathematical problems to show that it works as fast as the usual GAs. An adequate elite reservation technique for the robust GA is presented and applied to the robust GA for electromagnetic problems. Moreover, this method is shown to find solutions which are kept feasible against parameter changes. [ABSTRACT FROM AUTHOR]

This paper discusses mathematical properties of preconditioned finite-element matrices based on vector potential formulation (A method) and vector and scalar potential formulation (A-V method) for eddy-current problems. Numerical results show that A-V method with preconditioning is stable at all frequencies in contrast to A method. In this paper, this property is mathematically discussed by considering the diagonal scaling which is one of the simple preconditioning methods. In addition, regularization of A method is discussed. [ABSTRACT FROM AUTHOR]

The m-AINet is a modified version of the artificial immune network algorithm for single-objective and multimodal optimization (opt-AINet), with constraint-handling capability and improvements aiming to reduce the computational effort required by the original algorithm. In this paper we extend this algorithm for multiobjective problems by using the compromise programming approach to aggregate the objectives in the evaluation step. We adopt a compromise programming formulation that is theoretically able to map the whole Pareto front. The proposed multiobjective m-AINet is tested on the design of a loudspeaker with two objectives, showing promising results. [ABSTRACT FROM AUTHOR]

Robust optimization searches for optimal solutions which remain feasible under the effect of uncertainties. The conventional robust optimization, however, often requires great computational cost to find robust solutions. This paper introduces a novel robust optimization method based on genetic algorithm. In this method, individuals with "good" performance are selected and tested for their robustness for reduction of computational time. It is shown that the present method works in shorter time in comparison with the conventional robust optimization. [ABSTRACT FROM AUTHOR]

Topology optimization (TO) is a promising field for the design of industrial devices, since it can allow computational tools to develop creative solutions independently of initial topologies. However, the generation of smooth solutions, i.e., solutions that can be physically implemented, remains a challenge for most TO techniques available. In this paper, we discuss a set of operators that allow smoother topologies to be generated by an evolutionary topology optimization algorithm based on the artificial immune systems paradigm. The design of an electromagnetic device is used as a test case for the proposed methodology. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms can benefit from their association with local search operators, giving rise to hybrid or memetic algorithms. The cost of the local search may be prohibitive, particularly when dealing with computationally expensive functions. We propose the use of local approximations in the local search phase of memetic algorithms for optimization of cost functions. These local approximations are generated using only information already collected by the algorithm during the evolutionary process, requiring no additional evaluations. The local search improves some individuals of the population, hence speeding up the overall optimization process. We investigate the design of a loudspeaker magnet with seven variables. The results show the improvement achieved by the proposed combination of local learning and search within evolutionary algorithms. [ABSTRACT FROM AUTHOR]

When designing high-field superconducting magnets, such as the ones used in nuclear magnetic resonance, one is often interested in using the minimum winding volume, in order to reduce the construction costs. On the other hand, a highly homogeneous, highly regulated magnetic field is an essential requirement for this kind of system. Moreover, small variations in the design parameters are unavoidable in the construction of any equipment, and must be taken into account during the design process. Due to these many requirements, the design of magnets with high field homogeneity is a very difficult task for traditional optimization methods. Optimization methods based in the artificial immune systems paradigm are usually able to perform global as well as local exploration of the search space. The local search feature can be used to perform an estimation of the sensitivity of the candidate solutions, thus enabling the algorithm to search for points that represent robust devices at a reasonable computational cost. In this work, we explore the design of a high-field, highly homogeneous superconducting magnets by using an immune-based robust optimization algorithm. The algorithm is described, and applied to the optimization of the device. The analysis of the results shows the validity of the proposed method. [ABSTRACT FROM AUTHOR]

The TEAM benchmark problem 22 is an important optimization problem in electromagnetic design, which can be formulated as a constrained mono-objective problem or a multiobjective one with two objectives. In this paper, we propose a multiobjective version with three objectives, whose third objective is related to the quench constraint and the better use of the superconducting material. The formulation proposed yields results that provide new alternatives to the designer. We solved the formulation proposed using the multiobjective clonal selection algorithm. After that, we selected a particular solution using a simple decision making procedure. [ABSTRACT FROM AUTHOR]

In this paper, the self-organizing map is applied for visualization and clustering of optimal solutions in parameter spaces. The present method is tested for the simplified version of De Jong's function and TEAM workshop problem 22. It is shown that the present method successfully visualizes the distribution of optimal solutions, and allows us to classify the optimal solutions into clusters. Moreover, the optimal regions which include clustered optimal solutions in the parameter space can be obtained by the present method. [ABSTRACT FROM AUTHOR]