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In this paper, we propose a hybrid genetic algorithm (GA) for the optimal shape design of an axially symmetric dual-reflector antenna by combining the GA with the moving least square (MLS), which enhances the convergence rate and the global search performance. The MLS is used to construct local interpolation functions from non-uniform sample data and to estimate new superior positions. By combining these superior positions in the next generation, the MLS-GA shows better search performance for the global optimum and a faster convergence rate than those of the conventional GA. To verify the proposed MLS-GA, we applied it to the optimal shape design of the DRA at the W-band. [ABSTRACT FROM AUTHOR]

This paper applied the finite-element method (FEM) with topological data structures to the optimization of electrical devices. A generic modifiable graph data structure is built according to the graph-theoretic foundation for these data structures. The employed topological FEM avoids rebuilding of the mesh in traditional FEM method while geometrical variations happen. It is an effective algorithm which can save computer storage and working time when applied to successive optimization. The effectiveness is verified by numerical example and comparison of the topological FEM with traditional FEM on storage load and working time is shown in this paper. [ABSTRACT FROM AUTHOR]

Design of electromagnetic devices has multimodal, multidimensional, and constrained characteristics. Metaheuristic approaches are good choices for tackling these design problems owing to their simulation-based property. As many electromagnetic design problems require a long computing time to solve (even on modern computers) and many heuristic approaches have been created, the major goal of this paper is to improve the effectiveness and robustness of existing approaches. This paper proposes an algorithm with the ensemble of two composite differential evolution (DE) ingredients. One ingredient is biased toward exploration and the other is biased toward exploitation. The probability of choosing which ingredient to search for new solutions is adaptively updated based on the previous performance of each ingredient. The algorithm is applied to solve a loudspeaker design problem with promising performance when compared with DE, artificial bee colony, two improved artificial bee colony algorithms, and a randomly choosing ingredients algorithm. [ABSTRACT FROM AUTHOR]

This paper presents the fast topology optimization methods for rotating machines based on deep learning. The cross-sectional image of electric motors and their performances obtained during a multi-objective topology optimization based on the finite-element method and genetic algorithm (GA) is used for training of the convolutional neural network (CNN). Two different approaches are proposed: 1) CNN trained by preliminary optimization with a small population for GA is used for the main optimization with a large population and 2) CNN is used for screening of torque performances in the optimization with respect to the motor efficiency. [ABSTRACT FROM AUTHOR]

Backtracking search algorithm (BSA) is a novel evolutionary algorithm (EA) for solving real-valued numerical optimization problems. In this paper, an adaptive BSA (ABSA) is proposed to solve the optimization problem of an induction magnetometer (IM). In the adaptive algorithm, the probabilities of crossover and mutation are varied depending on the fitness values of the solutions to refine the convergence performance. The proposed ABSA will also be compared with basic BSA and other widely used EA algorithms. Simulation results show that ABSA is better able to solving the IM optimization problems. [ABSTRACT FROM AUTHOR]

Optimization metaheuristics is a powerful way to deal with many electromagnetic optimization problems. Their main advantages are that they don’t require gradient computation, they are more likely to give a global optimum solution and have a higher degree of exploration and exploitation ability. Recently, the symbiotic organisms search (SOS) algorithm was proposed to solve single-objective optimization problems. SOS mimics the symbiotic relationship among the living beings. In order to extend the classical mono-objective SOS algorithm approach, this paper proposes a new multiobjective SOS (MOSOS) based on nondominance and crowding distance criterion. Furthermore, an improved MOSOS (IMOSOS) based on normal (Gaussian) probability distribution function also was proposed and evaluated. Results on a multiobjective constrained brushless direct current (dc) motor design show that the MOSOS and IMOSOS present promising performance. [ABSTRACT FROM AUTHOR]

An optimization technique based on the combination of optimization algorithm and sensitivity analysis is discussed. The technique allows reducing the number of optimized parameters during the optimization process, which consequently reduces the search space and time of optimization. This paper explains the principle, benefits, and future challenges of this technique and illustrates its utilization with numerical experiments and a typical example. The algorithm is implemented in the framework OptiLab that represents a part of the application Agros2D developed by me. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid algorithm based on the genetic algorithm (GA) and the evolution strategy (ES) for the electromagnetic optimization problem. The GA is not good enough at times in searching the optimal solution from the view point of the convergence speed and the solution quality, while the ES has the risk of being trapped in a local minimum. The hybrid algorithm is composed of GA and ES in order to make up for these defects. First, we reached the vicinity of optimal solution using the GA. Then, the ES is used to find the accurate optimal solution. The switching point can be a main issue, which is also resolved in this paper. First, the performance of the convergence speed and the solution accuracy are comparatively tested using the known functions. In addition, the optimized design of the 2.45 GHz coplanar waveguide-fed circularly polarized antenna is carried out as a practical application. Only the GA and the hybrid algorithm reach the satisfactory value, and the more rapid convergence can be shown by the ES in this hybrid method after 380 iterations. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms (EAs) have become the standards and paradigms for solving inverse problems. However, their two inherited operations, namely, the crossover and mutation operations, are complicated and difficult, both in theory and in numerical implementations. In this regard, increasing efforts have been devoted to EAs which are based on probabilistic models (EAPMs) to overcome the shortcomings of available EAs. The population-based incremental learning (PBIL) is an EAPM; moreover, it can bridge the gap between machine learning and the EAs, hence enjoying several merits compared with other EAs. However, lukewarm efforts have been devoted to PBILs, especially the real coded PBILs, in the study of inverse problems in electromagnetics. In this regard, a novel real coded PBIL is being proposed in this paper. In the proposed real coded PBIL, a probability matrix is proposed to randomly generate a population, and the updating formulas for this probability matrix using the so far searched best solution and the best solution of the current population are introduced to strike a balance between convergence performance and solution quality. The proposed real coded PBIL algorithm is numerically experimented on several case studies and promising results are reported in this paper. [ABSTRACT FROM PUBLISHER]

Harmony search (HS) algorithm is an evolutionary optimization algorithm developed in an analogy with an improvisation process where musicians try to polish their pitches to obtain a better harmony. In this paper, a modified HS (MHS) algorithm is adapted to multi-objective optimization using external archiving, ranking with crowding distance, and control parameters tuning based on Ricker map to solve a transformer design optimization (TDO) problem with two competing objectives. Simulations applied to a TDO problem demonstrate the effectiveness of the proposed multi-objective MHS algorithm. Results indicate that, compared with other multi-objective HS algorithm, in terms of output quality, the proposed MHS is able to find competitive solutions with a good tradeoff between the design objectives. [ABSTRACT FROM PUBLISHER]

Brain storm optimization (BSO) is a novel population-based swarm intelligence algorithm based on the human brainstorming process. BSO has been proven feasible and has been successfully applied to benchmark problems in the electromagnetic field. In this paper, inspired by the mechanism of quantum theories, a novel variant of BSO algorithm, called quantum-behaved BSO (QBSO), is proposed to solve an optimization problem modeled for Loney’s solenoid problem. The new mechanism improves the diversity of population and also utilizes the global information to generate the new individual. Simulation results show that QBSO has better ability to jump out of local optima and perform better compared with the basic BSO. [ABSTRACT FROM AUTHOR]

This paper deals with design optimization of air-cored permanent magnet linear synchronous motors with overlapping windings to achieve high thrust per volume, high thrust per coils quantity, high motor constant, and low thrust ripple simultaneously for ultra-precise positioning stage with air-bearing. Based on accurate magnetic field model, motor parameters such as flux density, inductive electromotive force (EMF), thrust per volume, thrust per coils quantity, and thrust ripple are analyzed. A multiobjective optimization method with weight coefficients is proposed by applying the multiple population genetic algorithm. The design optimization is verified by 3-D finite element analysis and experimental results. [ABSTRACT FROM AUTHOR]

Improvements to overcome premature convergence of existing wind driven optimization algorithms are proposed. The specific measures being proposed include: 1) selection of the origin point of every parcel using dynamic and random tournament selection mechanisms to guarantee that there is a good balance between exploration and exploitation searches. In this paper, the “so far searched best solution” will be exploited to guide the movement of the randomly initialized parcels by introducing a newly designed mechanism; and a probabilistic mutation is designed; and 2) full utilization of the latest information accumulated from the searched history in order to guide the search toward the potential solutions to enhance convergences, and the “so far searched worst parcel” is used to shift the new parcel away from the parcel in issue. Numerical results on three case studies are reported to showcase the feasibility and the merit of the proposed method in solving both practical engineering design problems and mathematical test functions. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid, multiobjective optimization algorithm enabling global optimum tracking in permanent-magnet (PM) traction motor design. The methodology developed is based on the Artificial Bee Colony technique, strength Pareto evolutionary algorithm, and differential evolution strategy ensuring fast and reliable convergence to the optimal Pareto front. The effectiveness of the derived methodology is compared with other well-established and powerful algorithms from the literature through both appropriate test functions and an application example concerning an unequal teeth surface-mounted PM wheel motor design. [ABSTRACT FROM AUTHOR]

This paper proposes a novel Continuous Quantum Immune Clonal Optimization algorithm for thermal optimization on a 117 kW high-speed permanent-magnet generator (HSPMG). The proposed algorithm mixes the Quantum-Computation into the Immune-Cloning-Algorithm and causes better population diversity, higher global searching ability, and faster convergence which is approved by simulation results. Then, the improved algorithm is applied to seek an optimized slot groove and improve HSPMG thermal performance, where the 3-D fluid-thermal coupling analyses are processed with a multiobjective optimal group composed of the highest temperature and temperature difference. Both the proposed algorithm and the obtained conclusions are of significances in the design and optimization of the cooling system in electric machines. [ABSTRACT FROM AUTHOR]

In this paper, a novel hybrid multi-objective optimization algorithm based on the nondominated sorting genetic algorithm II for large array thinning is presented. The iterative fast Fourier transform (IFFT) technique with a judge factor is introduced into the optimizer to accelerate the convergence. The global characteristics of a genetic algorithm show its optimization capability in the early phase of the optimization process and the powerful local search ability of IFFT works in the late phase. Thus, this proposed algorithm can not only effectively avoid being trapped into the local optimum but also possess a fast convergence for large array thinning. Several representative examples of large planar thinned arrays validate the good performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

A finite-element analysis-based optimal design of an electric machine takes considerable time for its objective evaluation and has many local minima. Thus, selecting an appropriate global convergence optimization with fast convergence speed is necessary in the optimal design of an electric machine. In this paper, a novel global search optimization algorithm, mass ionized particle optimization (MIPO), is newly proposed. The MIPO is the population-based algorithm, which reflects the interactive force between the ionized particles. The global convergence and the convergence speed are validated by comparison with the particle swarm optimization, which have already been proved for its global convergence when applied to a well-known Goldstein–Price function as a benchmark function. In addition, the algorithm has been applied to the optimal design of an interior permanent magnet synchronous machine aiming for its torque ripple reduction. [ABSTRACT FROM AUTHOR]

Optimization of an electric machine is a nonlinear multi-variable problem. For optimization of the nonlinear multi-variable problem, many function evaluations are required, which in turn requires much time. To address this problem, we propose a novel optimization algorithm of which the convergence speed, accuracy, and reliability are superior compared to those of widely used conventional algorithms. The performance of the proposed algorithm is verified through mathematical test functions and applied to a practical optimization scenario of cogging torque minimization for an interior permanent magnet synchronous machine. [ABSTRACT FROM AUTHOR]

Stochastic algorithms are powerful global optimizers in topology optimizations (TOs). However, the low convergence speed and high computational cost, due to the large dimensionality of a TO problem, of such an approach are still two open issues to be addressed. To address these two issues, a TO methodology based on a modified quantum evolutionary algorithm is proposed. In the proposed methodology, an adaptive updating mechanism for the rotation gate is introduced to balance the exploration and exploitation searches; and a redistribution mechanism of design variables is developed to reduce the computational cost of the algorithm. The numerical results as reported validate the effectiveness and feasibility of the proposed methodology and demonstrate its advantages over a traditional ON/OFF method and its ancestor. [ABSTRACT FROM AUTHOR]

When a conventional optimization algorithm is applied to diverse problems, the performance is not guaranteed because the optimization algorithm is tuned properly to a specific problem. To address this problem, a novel strategy-selecting hybrid optimization algorithm (SSHOA) is proposed. The proposed algorithm can autonomously and intelligently establish a strategy, which offers a better fitting algorithm according to a varying problem situation. The efficiency, accuracy, and reliability of the SSHOA are verified via mathematical test functions. To confirm electromagnetic performance capabilities, the proposed algorithm is applied to the optimization of an outer rotor permanent magnet machine. [ABSTRACT FROM AUTHOR]

Even though evolutionary algorithm (EA) has now become the standard and paradigm for solving multi-objective design problems, the complexity of its genetic operation is, however, limiting its popularity in engineering applications. Hitherto, there has been insufficient research that addresses the inadequacy of EAs in extracting the characteristic landscape features of an objective function. However, increasing attentions have now been devoted to EAs based on probabilistic models (EAPMs) in computational intelligence studies. A real coded scalar population-based incremental learning algorithm, an EAPM, is proposed for multi-objective optimizations of electromagnetic devices. Major improvements include the design of a generating mechanism for new intermediate solutions, the selection of elite solutions to update the probability matrix, matrix updating formulations, and refinement mechanism for intervals to precisely generate intermediate solution. Also, a methodology to consider quantitatively both the number of improved objectives and the amount of improvements in a specified objective of multi-objective design problems is introduced in fitness assignments. Numerical results on a high frequency and a low inverse problem are reported to showcase the merits of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Transcranial magnetic stimulation (TMS) is non-invasive neuromodulation therapy which uses time-varying magnetic fields to induce electric fields within the patient’s brain, thus allowing for neural stimulation of the targeted region. While past studies have used finite-element analysis (FEA) to model the effects of stimulation on brain tissue, there have been limited studies which analyze the effects of the same stimulation on the neuron responses. We use a python package called NEST to model the populations of neurons which are healthy as well as those that have diminished or absent myelin sheath. We model diminished myelin sheath by increasing the capacitance of the neuron. We study the effects of TMS on the synaptic activity of these populations by utilizing clinical parameters specific to TMS. Furthermore, we compare our results to the models of brain tissue stimulation using the FEA software Sim4Life. Our results indicate that all neuron populations, regardless of their myelination state, retain some stimulation threshold which increases discretely as the myelin sheath diminishes. Using tissue analysis, we also computed the range of TMS current necessary to reach these stimulation thresholds for demyelinated populations. Furthermore, we find that the maximum-induced E-field on the cortical surface does not exceed 220 V/m for stimulation of highly demyelinated neuron populations. Therefore our study finds that although demyelinated neurons exhibit much lower nominal synaptic activity than healthy neurons, they are nevertheless responsive to TMS, and these stimulation thresholds can be reached without inducing an unsafe maximum E-field on the cortex. [ABSTRACT FROM AUTHOR]

Many real-world optimization problems have to be treated as multi-objective optimization problems. An approach, well established in recent years, is to find Pareto optimal configurations of the trial variables by detecting nondominated solutions with the help of a suitable vector optimization method. Alternatively, relying on scalar optimization methods (both stochastic or deterministic), a suitable objective function taking all objectives into account simultaneously has to be defined. Depending on the number of trial variables, a scalar objective function of that type will exhibit a considerable number of feasible local solutions besides the global one. Therefore, a useful scalar optimization strategy should be able to end up (with a high probability) in the best of all possible solutions in the given search space and additionally detect as many local solutions as possible. Some population-based stochastic methods are implicitly suited for that task; others can be enhanced to fulfill these requirements. Higher order evolution strategies have successfully been tuned for that kind of problem by introducing cluster sensitive recombination [niching higher order evolution strategy (NES)]. The firefly algorithm (FFA) mimics the behavior of fireflies, which use a kind of flashing light to communicate with other members of their species. Since the intensity of the light of a single firefly diminishes with increasing distance, the FFA is implicitly able to detect local solutions on its way to the best solution for a given scalar objective function. The FFA will be applied to the well-known Rastrigin test function and to a shielding/shunting electromagnetic problem with two and three objectives, respectively, and its results will be compared with the ones obtained with an NES. [ABSTRACT FROM AUTHOR]

A dynamic Taylor Kriging (DTK) is newly developed and combined with a multi-objective differential evolution algorithm to get a numerically efficient multi-objective optimization strategy. In the DTK, basis functions are not predefined but optimally selected so that the fitting error with the given sampling data may be minimized. In the developed multi-objective optimization algorithm, the DTK provides predicted objective function values as an alternative to direct finite-element analysis. The effectiveness of the proposed DTK and multi-objective optimization strategy are verified through applications to analytic example and TEAM 22. [ABSTRACT FROM PUBLISHER]

Magnetic fluid hyperthermia is a cancer therapy that requires a homogeneous magnetic field to heat nanoparticles localized in the treatment volume. The efficacy of nanoparticles heating is studied using cells cultured in Petri dishes. The automated optimal design of the inductor to generate the magnetic field to heat nanoparticles in cells cultured in Petri dishes is presented, exploiting field analysis and a new version of an optimization algorithm. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is a population-based stochastic search algorithm inspired from the natural behavior of bird flocking or fish schooling for searching their foods. Due to its easiness in numerical implantations, PSO has been widely applied to solve a variety of inverse and optimization problems. However, a PSO is often trapped into local optima while dealing with complex and real world design problems. In this regard, a new modified PSO is proposed by introducing a mutation mechanism and using dynamic algorithm parameters. According to the proposed mutation mechanism, one particle is randomly selected from all personal best ones to generate some trial particles to preserve the diversity of the algorithm in the final searching stage of the evolution process. Moreover, the random variations in the two learning factors will help the particles to reach an optimal solution. In addition, the dynamic variation in the inertia weight will facilitate the algorithm to keep a good balance between exploration and exploitation searches. The numerical experiments on different case studies have shown that the proposed PSO obtains the best results among the tested algorithms. [ABSTRACT FROM AUTHOR]

A new optimization method, combining design of experiments with evolutionary computing, is proposed. It handles a set of design variables, the size of which changes during the process. Initially, the most sensitive variables are activated; subsequently, the whole set of variables is activated. The optimal synthesis of a magnetic field for magnetofluid treatment is considered as the case study. [ABSTRACT FROM AUTHOR]

Bit-patterned media recording (BPMR) is a magnetic data storage solution where the medium is patterned into nanoscale islands, each representing one bit of data. Write errors can occur in BPMR, especially where islands have position, shape, or magnetic properties that are very different from the mean, i.e., those islands in the tails of any parameter probability density functions (PDFs). We have used a model of BPMR that incorporates variable shape parameter PDFs to study write errors. This shows that the precise shape of the tails of the distributions is critical in determining the error performance. We conclude that when characterizing samples of media for BPMR, it is not sufficient to assume a Gaussian distribution and determine the mean and standard deviation by fitting; the tails of parameter PDFs must be characterized precisely. [ABSTRACT FROM AUTHOR]