Electromagnetic Optimization Research Articles

A water cycle-based error minimization technique in predicting the bearing capacity of shallow foundation

Selecting the appropriate training technique is a significant step in utilizing intelligent approaches. It becomes even more important when it comes to critical problems like analyzing the bearing capacity of foundations. This study investigates the feasibility of a capable metaheuristic algorithm, called the water cycle algorithm (WCA), for training a multi-layer perceptron (MLP). The WCA-MLP is applied to a large finite element dataset to predict the settlement. The results of this model are compared with electromagnetic field optimization (EFO) and shuffled complex evolution (SCE) benchmarks. With reference to the obtained Pearson correlation factors (larger than 0.88 in all stages), all employed models are suitable for the mentioned objective. Moreover, it was observed that the training error of the WCA was 5.84 and 3.89% smaller than the EFO and SCE, respectively. Likewise, the accuracy of the WCA-MLP was 1.85 and 2.04% larger in the testing phase. Also, a predictive equation is finally elicited for practical applications in compatible circumstances.

An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework

Proper management of solar energy as an effective renewable source is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO), is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for nonlinearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development.

Quick integrative optimizers for minimizing the error of neural computing in pan evaporation modeling

To achieve an efficient methodology for approximating pan evaporation (EP), this study offers two metaheuristic-integrated predictors. Shuffled complex evolution (SCE) and electromagnetic field optimization (EFO) are two of the fastest metaheuristic algorithms that are synthesized with artificial neural network (ANN). By doing this, the ANN is optimized in a noticeably shorter time compared to its integration with other metaheuristic techniques. Five-year climatic data of the Bakersfield station (California, USA) with an 80:20 ratio are used for developing and testing the methods. The proposed hybrids are implemented with appropriate population sizes (20 and 35 for the SCE and EFO, respectively) and their results are compared to a single ANN. Accuracy evaluation (correlation coefficients > 0.99) professed that the neural network with both conventional and sophisticated trainers is a competent approach for the EP simulation. Besides, it was observed that the error of prediction by the ANN-SCE and ANN-EFO is 6.02 and 9.27% lower than the single ANN, respectively. Therefore, the used strategies can enhance the applicability of the ANN. The time elapsed in the optimization using SCE and EFO was 479.0 and 281.9 s, respectively. A comparison between these algorithms revealed that the EFO is both a faster and more accurate optimizer. The ANN-EFO is accordingly recommended as a new efficient model for predicting the EP.

MR Brain Image Segmentation to Detect White Matter, Gray Matter, and Cerebro Spinal Fluid Using TLBO Algorithm

This paper proposes an advanced and precise technique for the segmentation of Magnetic Resonance Image (MRI) of the brain. Brain MRI segmentation is to be familiar with the anatomical structure, to recognize the deformities, and to distinguish different tissues which help in treatment planning and diagnosis. Nature’s inspired population-based evolutionary algorithms are extremely popular for a wide range of applications due to their best solutions. Teaching Learning Based Optimization (TLBO) is an advanced population-based evolutionary algorithm designed based on Teaching and Learning process of a classroom. TLBO uses common controlling parameters and it won’t require algorithm-specific parameters. TLBO is more appropriate to optimize the real variables which are fuzzy valued, computationally efficient, and does not require parameter tuning. In this work, the pixels of the brain image are automatically grouped into three distinct homogeneous tissues such as White Matter (WM), Gray Matter (GM), and Cerebro Spinal Fluid (CSF) using the TLBO algorithm. The methodology includes skull stripping and filtering in the pre-processing stage. The outcomes for 10 MR brain images acquired by utilizing the proposed strategy proved that the three brain tissues are segmented accurately. The segmentation outputs are compared with the ground truth images and high values are obtained for the measure’s sensitivity, specificity, and segmentation accuracy. Four different approaches, namely Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Bacterial Foraging Algorithm (BFA), and Electromagnetic Optimization (EMO) are likewise implemented to compare with the results of the proposed methodology. From the results, it can be proved that the proposed method performed effectively than the other.

Synchronous Reluctance Machines for Automotive Cooling Fan Systems: Numerical and Experimental Study of Different Slot-Pole Combinations and Winding Types

In this paper a side-by-side comparison between synchronous reluctance machines (SynRMs) with concentrated and distributed windings is performed. The characteristics, parameters, and the installation space of a permanent magnet synchronous machine (PMSM) with concentrated windings used in a 13 V automotive cooling fan system (CFMs) are used as requirements and specifications. For that, eight SynRMs with different stator and rotor topologies are investigated and optimized by means of FE-based electromagnetic optimization. Knowing the challenges associated with the development of mechanically stable SynRM rotor structures, for two selected cross-sections in view of being prototyped, designs checks are performed to ensure robust operation at up to two times the required operating speed. The simulated results were verified by means of measurements performed using two different types of loading systems, i.e., with the real ventilator and using a DC machine as a load. Based on this, the relative differences between all three motor technologies in terms of important quantities (e.g., torque-speed characteristic, torque ripple, efficiency, power factor and ultimately the size) are highlighted.

Efficient Image Segmentation of Natural Images with Noise using Energy Curved Based on Electromagnetism Optimization Algorithm

The method of digital image processing is an important step for extraction features from images, especially details from the medical image by dividing the image into various regions. In the last two decades, many significant image segmentation schemes modeled for different applications like medical to weather forecasting by images from the satellite. From available methods, multilevel thresholding of pixel values using the histogram of an image is an exceedingly important method developed for vide range of applications. Otsu’s method is the primer multilevel thresholding method based on the maximizing the inter-class variance based on the histogram. In Otsu’s method, the pixels of an image are grouped into different regions based on threshold levels calculated on the histogram of the image. All the pixels are segmented into two groups based on threshold level in the case of bi-level thresholding. The effectiveness of segmentation is depends on threshold levels, efficiency can be enhanced by computing optimized threshold level, Electromagnetism Optimization (EMO) technique used for this purpose in this paper. The techniques based on histograms of image are not holding the spatial particulars of contextual information for computing the optimized threshold levels, to nullify this shortcoming Energy Curve is used as an alternative of histogram, which is restore the special relation of pixels with its neighbor pixels. To examine the results several benchmark images considered, the final results shows that the method with Otsu’s technique with EMO on energy curve is giving better results as compared with methods based on the image histogram.

Design of Stackable Electromagnetic Actuator and Optimization of Mover to Improve Characteristics

Design of Stackable Electromagnetic Actuator and Optimization of Mover to Improve Characteristics

Yield-Constrained Optimization Design Using Polynomial Chaos for Microwave Filters

Yield optimization aims at finding microwave filter designs with high yield under fabrication tolerance. The electromagnetic (EM) simulation-based yield optimization methods are computationally expensive because a large number of EM simulations is required. Moreover, the microwave filter design usually requires several performance objectives to be met, which is not considered by the current yield optimization methods for microwave filters. In this paper, an efficient yield-constrained optimization using polynomial chaos surrogates (YCOPCS) is employed for microwave filters considering multiple objectives. In the YCOPCS method, the low-cost and high-accuracy of polynomial chaos is used as a surrogate. An efficient yield-constrained design framework is implemented to obtain the optimal design solution. Two numerical examples demonstrate the performance of the YCOPCS method, including a coupling matrix model of a fourth-order filter with cascaded quadruplet topology and an EM simulation model of a microwave waveguide bandpass filter. The numerical results show that the YCOPCS method can obtain the filter designs with higher yield and reduce EM simulations by 80% compared to Monte Carlo-based yield optimization in all testing examples.

Machine Learning in Calorimeter optimization

The optimization of big industrial setups and the accompanying detailed simulations of internal physical processes require complex and time-consuming simulation calculations. We propose a versatile approach that can alleviate difficulties in solving this problem and show this using an example of electromagnetic calorimeter optimization at a Large Hadron Collider experiment. Our approach consists of a block representation of the calorimeter optimization process from setting sensitive characteristics of modules and their locations to obtaining a quality metric and applying machine learning methods. The main blocks are signal and background particles generation and their propagation to the calorimeter, the generation of electromagnetic showers of signal and noise in modules with a given technology, the combination of signal and noise with the simulation of different luminosities, the energy and spatial reconstruction of the signal and obtaining the final metric. This approach allows us to evaluate the operational characteristics of the calorimeter and find a more suitable configuration with the necessary quality without extensive use of time-consuming resources.

Cauchy-Gaussian pigeon-inspired optimisation for electromagnetic inverse problem

The optimisation of electromagnetic inverse problems could be attributed to a constraint nonlinear programming problem. Loney's solenoid problem is one of the electromagnetic inverse benchmarks in the magnetic field. Parameters such as the structure and medium are necessary to be designed based on the required magnetic properties. In this paper, an improved variant of pigeon-inspired optimisation (PIO) algorithm based on Cauchy distribution and Gaussian distribution, named Cauchy-Gaussian pigeon-inspired optimisation (CGPIO), is proposed to solve electromagnetic inverse problems. The PIO algorithm is a bio-inspired swarm intelligence optimisation algorithm, which imitates the homing process of pigeons. To improve the convergence efficiency of the basic PIO algorithm, two operators including Cauchy distribution and Gaussian distribution are utilised. Comparative results show the suitability and superiority of CGPIO algorithm for electromagnetic optimisation.

Branch and Bound Algorithm Based on Prediction Error of Metamodel for Computational Electromagnetics

Metamodels proved to be a very efficient strategy for optimizing expensive black-box models, e.g., Finite Element simulation for electromagnetic devices. It enables the reduction of the computational burden for optimization purposes. However, the conventional approach of using metamodels presents limitations such as the cost of metamodel fitting and infill criteria problem-solving. This paper proposes a new algorithm that combines metamodels with a branch and bound (B&B) strategy. However, the efficiency of the B&B algorithm relies on the estimation of the bounds; therefore, we investigated the prediction error given by metamodels to predict the bounds. This combination leads to high fidelity global solutions. We propose a comparison protocol to assess the approach’s performances with respect to those of other algorithms of different categories. Then, two electromagnetic optimization benchmarks are treated. This paper gives practical insights into algorithms that can be used when optimizing electromagnetic devices.

A developed hybrid forecasting system for energy consumption structure forecasting based on fuzzy time series and information granularity

The energy consumption structure has a crucial influence on the sustainable development of the economy and on the environment, and it has drawn the attention of scholars and managers. The forecasting of different types of energy consumption, especially small-sample forecasting, has been a challenging task because of the limitation of the sample size. Thus, in this study, a novel forecasting system based on fuzzy time series that is appropriate for small-sample forecasting was developed. Specifically, the fuzzy time series, which deals with the fuzzy set, is applied as the forecasting program. In fuzzy time series forecasting, the information granularity and fuzzy c-means clustering are utilized for fuzzification. Moreover, an improved chaotic electromagnetic field optimization algorithm is applied to search for the optimal parameters of the information granularity. The experiments and comparison verified that the proposed forecasting system has an excellent performance in energy consumption forecasting with great accuracy and stability, providing accurate forecasting for the energy consumption structure.

650 MHz elliptical superconducting RF cavities for CiADS Project

650 MHz multicell superconducting radio frequency (SRF) elliptical cavities are proposed for effective acceleration of proton beam in the Chinese initiative Accelerator Driven Subcritical System (CiADS). Two families of such cavities will be used in the driver superconducting linac to accelerate the H+ beam from 175 MeV to 500 MeV, with the possibility to upgrade the energy to 1 GeV and higher. Detail designs of a six-cell βopt = 0.62 elliptical cavity and a five-cell βopt = 0.82 elliptical cavity will be presented in this paper, including the multi-parameter electromagnetic design and optimization, high order modes analyses, multipacting simulations, mechanical and engineering analyses. Sub-systems of the cavity such as the fundamental power coupler antenna tip design and mechanical design of cavity-helium vessel-tuner system will also be discussed in detail. Three single cell prototype cavities were fabricated and vertical tested to verify the electromagnetic properties, the fabrication and post-processing technologies. Four multi-cell prototype cavities are under manufacturing and expected to be ready in 2021.

Advanced Cognition-Driven EM Optimization Incorporating Transfer Function-Based Feature Surrogate for Microwave Filters

This article proposes an advanced cognition-driven electromagnetic (EM) optimization incorporating transfer function-based feature surrogate for EM optimization of microwave filters. The proposed optimization technique addresses the situations where the response of the starting point for design optimization is far away from the design specifications. This article proposes to extract transfer function-based feature parameters for optimization to address the challenge that the features cannot be clearly and explicitly identified from the filter response. Multiple transfer function-based feature parameters are extracted and used to develop the feature surrogate model for the proposed cognition-driven optimization. Furthermore, we derive new objective functions for the cognition-driven optimization directly in the feature space. The proposed cognition-driven optimization incorporating transfer function-based feature surrogate can achieve faster convergence than the existing feature-assisted EM optimization methods. Two examples of EM optimizations of microwave filters are used to demonstrate the proposed technique.

Electromagnetic noise analysis and optimization for permanent magnet synchronous motor used on electric vehicles

PurposeElectromagnetic noise of permanent magnet synchronous motor (PMSM) seriously affects the sound quality of electric vehicles (EVs). This paper aims to present a comprehensive process for the electromagnetic noise analysis and optimization of a water-cooled PMSM.Design/methodology/approachFirst, the noises of an eight-pole 48-slot PMSM in at speeds up to 10,000 rpm are measured. Furthermore, an electromagnetic-structural-acoustic model of the PMSM is established for multi-field coupling simulations of electromagnetic noises. Finally, the electromagnetic noise of the PMSM is optimized by using the multi-objective genetic algorithm, where a multi-objective function related to the slot width of PMSM stator is defined for radial electromagnetic force (REF) optimization.FindingsThe experimental results show that main electromagnetic noises are the 8n-order (n = 1, 2, 3, …) and 12-order noises. The simulated results show that the REFs are mainly generated by the 8n-order (n = 1, 2, 3, 4, 5, 6) vibrations, especially those of the 8th, 16th, 24th and 32th orders. The 12-order noise is a mechanical noise, which might be caused by the bearings and other structures of the PMSM. Comparing the simulated results before and after optimization, both the REFs and electromagnetic noises are effectively reduced, which suggests that an appropriate design of stator slot is important for reducing electromagnetic noise of the PMSM.Originality/valueIn view of applications, the methods proposed in this paper can be applied to other types of PMSM for generation mechanism analysis of electromagnetic noise, optimal design of PMSM and thereby noise improvement of EVs.

Fuzzy Electromagnetism Optimization (FEMO) and its application in biomedical image segmentation

In this work, a new unsupervised classification approach is proposed for the biomedical image segmentation. The proposed method will be known as Fuzzy Electromagnetism Optimization (FEMO). As the name suggests, the proposed approach is based on the electromagnetism-like optimization (EMO) method. The EMO method is extended, modified, and combined with the modified type 2 fuzzy C-Means algorithm to improve its efficiency especially for biomedical image segmentation. The proposed FEMO method uses fuzzy membership and the electromagnetism-like optimization method to locate the optimal positions for the cluster centers. The proposed FEMO approach does not have any dependency on the initial selection of the cluster centers. Moreover, this method is suitable for the biomedical images of different modalities. This method is compared with some standard metaheuristics and evolutionary methods (e.g. Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Electromagnetism-like optimization (EMO), Ant Colony Optimization (ACO), etc.) based image segmentation approaches. Four different indices Davies–Bouldin, Xie–Beni, Dunn and β index are used for the comparison and evaluation purpose. For the GA, PSO, ACO, EMO and the proposed FEMO approach, the optimal average value of the Davies–Bouldin index is 1.833578359 (8 clusters), 1.669359475 (3 clusters), 1.623119284 (3 clusters), 1.647743907 (4 clusters) and 1.456889343 (3 clusters) respectively. It shows that the proposed approach can efficiently determine the optimal clusters. Moreover, the results of the other quantitative indices are quite promising for the proposed approach compared to the other approaches The detailed comparison is performed in both qualitative and quantitative manner and it is found that the proposed method outperforms some of the existing methods concerning some standard evaluation parameters.

Parallel Gradient-Based EM Optimization for Microwave Components Using Adjoint- Sensitivity-Based Neuro-Transfer Function Surrogate

This article proposes a novel parallel gradient-based electromagnetic (EM) optimization approach to microwave components using adjoint-sensitivity-based neuro-transfer function (neuro-TF) surrogate. In the proposed technique, the surrogate model is trained using not only the input-output behavior but also the adjoint sensitivity information generated from the EM simulation simultaneously. By exploiting adjoint EM sensitivity for surrogate modeling, the proposed technique can obtain accurate surrogate models with larger valid range using the same amount of fine model evaluations compared with the existing gradient-based surrogate optimization without adjoint sensitivity. Furthermore, because the surrogate model is developed using adjoint EM sensitivity, the gradients calculated using the developed surrogate model in the proposed technique are much more accurate. The accurate gradients lead to further speedup of the surrogate optimization and improved quality of surrogate optimal solution in each surrogate optimization iteration. Since the surrogate model is valid in a large neighborhood and the gradients are sufficiently accurate, the proposed technique can achieve the optimal EM solution faster than the existing gradient-based surrogate optimization without adjoint sensitivity. Three examples of EM optimizations of microwave components are used to demonstrate the proposed technique.

Status and Development Trend of Active Sub-arrays Structure Design in Active Phased Array Antenna

Active sub-arrays structure design is an important part of phased array radar design. The basic architecture and composition are introduced, especially the different between brick, tile and chip sub-arrays are discussed. Integrated design of radiating unit and sub-array, intelligent health detection system and electromagnetic compatibility, microelectronics and topology optimization design, application of new materials, which considered to be four key technologies, are also pointed out.

A new optimization algorithm applied in electromagnetics — Maxwell’s equations derived optimization (MEDO)

In this paper, a novel global optimization algorithm, named as Maxwell’s equations derived optimization (MEDO), is proposed. Using the Maxwell’s equations to analyse the behaviors of the time-varying current, the Ampere force is obtained from Fleming’s left hand rule. MEDO introduces an ‘Ampere force’ term, which is derived from Maxwell’s equations and is rigorous in physics, to drive the variables to the global optimal solution in the search space. In addition, introducing ‘gravity’ to MEDO can increase the stability of the optimizations. 11 classical benchmarks are tested, and results show that MEDO can always converge to numerical optimal solutions. To evaluate the proposed MEDO in solving the electromagnetic problems, four practical engineering applications are considered including the linear antenna array synthesis, frequency selected surface optimization, numerical dispersion reduction for finite-difference method, and parameters extraction of typical waveform. These examples are significant in electromagnetics, but tough to be solved because of their high dimensionality and strong nonlinearity. Numerical results show that MEDO can outperform several classic optimization methods, like wind driven optimization (WDO) and particle swarm optimization (PSO). Therefore, the electromagnetics-inspired MEDO is robust and of great potential in solving the electromagnetic optimization problems.

Manufacturing Challenges of a Modular Transverse Flux Alternator for Aerospace

This paper presents the manufacturing challenges of a transverse flux alternator for an aerospace application. For fault tolerance, four independent isolated phases are required to deliver a specific power at low speeds, whilst at over speed, there is a strict limit on the short circuit current. A transverse flux machine (TFM) was selected due to its high inductance combined with the modular nature of separate phases lending itself to fault tolerance. The stator consists of pressed soft magnetic composite (SMC) segments. The authors explore the electromagnetic, mechanical, and assembly design challenges of the machine. It is shown that mechanical design aspects of the segments are of equal importance to the electromagnetic design and optimization. Simple design choices have allowed the same component to be used as all the stator segments, despite the requirement of a 90° electrical phase difference between phases and a tooth offset of 180° electrical within each phase.