Electromagnetic Field Optimization Research Articles

Fast hybrid methods for modeling landslide susceptibility in Ardal County

Recently, machine learning models have received huge attention for environmental risk modeling. One of these applications is landslide susceptibility mapping which is a necessary primary step for dealing with the landslide risk in prone areas. In this study, a conventional machine learning model called multi-layer perceptron (MLP) neural network is built upon advanced optimization algorithms to achieve a firm prediction of landslide susceptibility in Ardal County, West of Iran. The used geospatial dataset consists of fourteen conditioning factors and 170 landslide events. The used optimizers are electromagnetic field optimization (EFO), symbiotic organisms search (SOS), shuffled complex evolution (SCE), and electrostatic discharge algorithm (ESDA) that contribute to tuning MLP’s internal parameters. The competency of the models is evaluated using several statistical methods to provide a comparison among them. It was discovered that the EFO-MLP and SCE-MLP enjoy much quicker training than SOS-MLP and ESDA-MLP. Further, relying on both accuracy and time criteria, the EFO-MLP was found to be the most efficient model (time = 1161 s, AUC = 0.879, MSE = 0.153, and R = 0.657). Hence, the landslide susceptibility map of this model is recommended to be used by authorities to provide real-world protective measures within Ardal County. For helping this, a random forest-based model showed that Elevation, Lithology, and Land Use are the most important factors within the studied area. Lastly, the solution discovered in this study is converted into an equation for convenient landslide susceptibility prediction.

Model Predictive Control Design for Electric Vehicle Based on Improved Physics-Inspired Optimization Algorithms

Abstract: This paper introduces the mathematical model of the leader-follower electric vehicle (EV). Consequently, the system was analyzed to obtain stability and performance. Model Predictive Control (MPC) is also proposed to fix the EV system issues. Moreover, two optimization algorithms are applied to optimize the performance of the MPC: electrically charged particle optimization (ECPO) and improved chaotic electromagnetic field optimization (ICEFO). The MPC scheme is based on the Adaptive Cruise Control System (ACCS), applied to two vehicles: the leader and follower. In this context, the simulation results of both optimization methods with the MPC scheme are presented in the result section. Finally, a comparison is made to show the proposed controller’s effectiveness with the improved optimization algorithms. Also, the ACC electric vehicle tracking system was achieved at 98% with the reference input.

A novel ensemble approach for estimating the competency of bank telemarketing

Having a reliable understanding of bank telemarketing performance is of great importance in the modern world of economy. Recently, machine learning models have obtained high attention for this purpose. In order to introduce and evaluate cutting-edge models, this study develops sophisticated hybrid models for estimating the success rate of bank telemarketing. A large free dataset is used which lists the clients’ information of a Portuguese bank. The data are analyzed by four artificial neural networks (ANNs) trained by metaheuristic algorithms, namely electromagnetic field optimization (EFO), future search algorithm (FSA), harmony search algorithm (HSA), and social ski-driver (SSD). The models predict the subscription of clients for a long-term deposit by evaluating nineteen conditioning parameters. The results first indicated the high potential of all four models in analyzing and predicting the subscription pattern, thereby, revealing the competency of neuro-metaheuristic hybrids. However, comparatively speaking, the EFO yielded the most reliable approximation with an area under the curve (AUC) around 0.80. FSA-ANN emerged as the second-accurate model followed by the SSD and HSA with respective AUCs of 0.7714, 0.7663, and 0.7160. Moreover, the superiority of the EFO-ANN is confirmed against several conventional models from the previous literature, and finally, it is introduced as an effective model to be practically used by banking institutions for predicting the likelihood of deposit subscriptions.

Predicting water quality through daily concentration of dissolved oxygen using improved artificial intelligence

As an important hydrological parameter, dissolved oxygen (DO) concentration is a well-accepted indicator of water quality. This study deals with introducing and evaluating four novel integrative methods for the prediction of DO. To this end, teaching–learning-based optimization (TLBO), sine cosine algorithm, water cycle algorithm (WCA), and electromagnetic field optimization (EFO) are appointed to train a commonly-used predictive system, namely multi-layer perceptron neural network (MLPNN). The records of a USGS station called Klamath River (Klamath County, Oregon) are used. First, the networks are fed by the data between October 01, 2014, and September 30, 2018. Later, their competency is assessed using the data belonging to the subsequent year (i.e., from October 01, 2018 to September 30, 2019). The reliability of all four models, as well as the superiority of the WCA-MLPNN, was revealed by mean absolute errors (MAEs of 0.9800, 1.1113, 0.9624, and 0.9783) in the training phase. The calculated Pearson correlation coefficients (RPs of 0.8785, 0.8587, 0.8762, and 0.8815) plus root mean square errors (RMSEs of 1.2980, 1.4493, 1.3096, and 1.2903) showed that the EFO-MLPNN and TLBO-MLPNN perform slightly better than WCA-MLPNN in the testing phase. Besides, analyzing the complexity and the optimization time pointed out the EFO-MLPNN as the most efficient tool for predicting the DO. In the end, a comparison with relevant previous literature indicated that the suggested models of this study provide accuracy improvement in machine learning-based DO modeling.

Retracted: Electromagnetic Field Analysis and Optimization Method of High-Temperature Superconducting Transformer under the Influence of Abnormal Voltage

Retracted: Electromagnetic Field Analysis and Optimization Method of High-Temperature Superconducting Transformer under the Influence of Abnormal Voltage

Finite Element Analysis and Electromagnetic Field Optimization of Linear Synchronous Motor in High-Speed Maglev Systems

Finite Element Analysis and Electromagnetic Field Optimization of Linear Synchronous Motor in High-Speed Maglev Systems

Combination of electromagnetic field and harris hawks optimization algorithms with optimization to optimization structure and its application for optimum power flow

ABSTRACT Electromagnetic Field Optimization (EFO) and Harris Hawk Optimization (HHO) algorithms are combined with the optimization to optimization (OtoO) approach, and the EFO-HHO algorithm pair is presented in this study. EFO method was used as the essential algorithm and HHO method was used as the auxiliary algorithm according to the OtoO structure. The constant parameters (R_rate, Ps_rate, P_field, N_field) of the EFO algorithm that affect the optimization performance are optimized with the HHO optimization algorithm for the related optimization problem. The proposed method was tested on 10 different benchmark functions according to different dimensional (30, 50100). The EFO-HHO algorithm pair can produce better results than the existing literature, especially in cases of increased dimension with the proposed approach. In addition to these, the OPF problem was tested on the IEEE 30 test bus system for the engineering application of the proposed method. The results are compared with the existing literature results. As it can be seen from the results, it has been shown on the real engineering problem that the optimization performance can be increased with the OtoO approach without changing the basic philosophy of the EFO algorithm.

A Generalizable Indoor Propagation Model Based on Graph Neural Networks

A surrogate model that “learns” the physics of radio wave propagation is indispensable for the efficient optimization of communication network coverages and comprehensive electromagnetic field (EMF) exposure assessments. The capability of a model to predict reasonable outputs given an input that is beyond the data with which the model is trained, namely, “generalizability”, is a fundamental challenge and a key factor for its practical deployment. In this paper, by leveraging the concept of graph neural networks (GNNs), a prediction model for indoor propagation that is “generalized” to not only transmitter positions but also new geometries is presented. We demonstrate that a geometry and a transmitter antenna can be modeled as a graph with all necessary information being included, and a GNN can acquire the knowledge of propagation physics through “learning” from these graphs. We further show that the model can be generalized to new geometry shapes, beyond the shape (square) for model training. We provide useful information on how to obtain an acceptable accuracy for different scenarios. We also discuss the potential solutions to further improve the model’s capability.

Estimating Compressive Strength of Concrete Using Neural Electromagnetic Field Optimization.

Concrete compressive strength (CCS) is among the most important mechanical characteristics of this widely used material. This study develops a novel integrative method for efficient prediction of CCS. The suggested method is an artificial neural network (ANN) favorably tuned by electromagnetic field optimization (EFO). The EFO simulates a physics-based strategy, which in this work is employed to find the best contribution of the concrete parameters (i.e., cement (C), blast furnace slag (SBF), fly ash (FA1), water (W), superplasticizer (SP), coarse aggregate (AC), fine aggregate (FA2), and the age of testing (AT)) to the CCS. The same effort is carried out by three benchmark optimizers, namely the water cycle algorithm (WCA), sine cosine algorithm (SCA), and cuttlefish optimization algorithm (CFOA) to be compared with the EFO. The results show that hybridizing the ANN using the mentioned algorithms led to reliable approaches for predicting the CCS. However, comparative analysis indicates that there are appreciable distinctions between the prediction capacity of the ANNs created by the EFO and WCA vs. the SCA and CFOA. For example, the mean absolute error calculated for the testing phase of the ANN-WCA, ANN-SCA, ANN-CFOA, and ANN-EFO was 5.8363, 7.8248, 7.6538, and 5.6236, respectively. Moreover, the EFO was considerably faster than the other strategies. In short, the ANN-EFO is a highly efficient hybrid model, and can be recommended for the early prediction of the CCS. A user-friendly explainable and explicit predictive formula is also derived for the convenient estimation of the CCS.

Maximum power point tracking of a standalone photovoltaic system using electromagnetic field optimization algorithm

Maximum power point tracking of a standalone photovoltaic system using electromagnetic field optimization algorithm

A skin cancer diagnosis system for dermoscopy images according to deep training and metaheuristics

One of the most common kinds of cancer in the United States is skin cancer. This kind of cancer occurs when the cells that make up the skin layer grow uncontrollably and divide rapidly. To avoid the skin cancer harms, it is better to detect it in the early stages. Due to complexity of the diagnosis, some times, the experts provide an incorrect diagnosis about this cancer. Therefore, proposing a computer-aided system, not as an independent system, but as an auxiliary tool along with the physicians, can help them in more accurate detection of this kind of cancer. This study introduced an optimal and efficient computer-assisted system for skin cancer analysis. Here, deep training and metaheuristic techniques are used for this purpose. The main idea is to provide a Deep Belief Network (DBN), based on an improved metaheuristic technique, called Modified Electromagnetic Field Optimization Algorithm (MEFOA) to provide a strong diagnosis system for the images of skin cancer. The function of the proposed method is proved through a comparison between its results and some other related works.

Electromagnetic Field Optimization Based Selective Harmonic Elimination in a Cascaded Symmetric H-Bridge Inverter

Multilevel inverters (MLIs), both symmetrical and asymmetrical, have shown to be useful in a number of applications. Continuous improvements in output voltage waveform control and converter size reduction have made this practicable. The output voltage is managed using a low frequency modulation technique called selective harmonic elimination. This paper investigates an unique selective harmonic elimination (SHE) control that uses electromagnetic field optimization (EFO). The major features of the EFO guarantee that the targeted harmonics are removed via computation of the ideal angles, such as its easier compilation procedure and capacity for single-stage local and global searches. Additionally, a comparison with well-known algorithms namely Genetic Algorithm and Differential Evolution in accessing performance based on Total Harmonic Distortion demonstrates the EFO’s competence. The suggested algorithm’s performance has been tested using a symmetric cascaded H-Bridge MLI structure. In the MATLAB/Simulink environment, simulation analysis is performed, validating the viability of the created system. To further show the effectiveness of the suggested approach, experimental testing using low switching frequency control methods has been carried out in a dynamic setting.

Electromagnetic Field Analysis and Optimization Method of High-Temperature Superconducting Transformer under the Influence of Abnormal Voltage

High-temperature superconducting transformers are an important research topic of superconducting technology in power applications, and electromagnetic field analysis and optimization are the basis for the design and application of high-temperature superconducting transformers. The electromagnetic field analysis of high-temperature superconducting transformers should consider the superconducting properties of the materials, that is, the properties of critical current and magnetic field. This paper aims to study the electromagnetic field analysis and optimization method of high-temperature superconducting transformer under the influence of abnormal voltage. Due to the energy loss of high-temperature superconducting transformers, in order to study the economy and reliability of high-temperature superconducting transformers, in this paper, the core loss, winding AC loss, and coil power consumption of high-temperature superconducting transformers are analyzed under normal operation and short-circuit fault conditions, respectively. The power and stress on the windings are analyzed. In order to take into account the current-carrying capacity, short-circuit loss, and short-circuit electromotive force of superconducting windings in normal operation, a concentrically placed double-cake coil structure is selected in this paper, and according to different optimization objectives, a global optimization method is used to evaluate the structure of the coil. The structural parameters of the high-temperature superconducting transformer are optimized, including the structural parameters of the magnetic conducting ring. It is found that that abnormal voltage will affect the electromagnetic field of high-temperature superconducting transformers, including winding circulating current, leakage magnetic field, and current distribution.

Optimal Power Flow Incorporating Thyristor-Controlled Series Capacitors Using the Gorilla Troops Algorithm

The optimal power flow issue (OPFI) can be solved in this work using the recently developed algorithm, gorilla troops algorithm (GTA). The goal of OPFI is to reduce numerous functions such as minimizing fuel costs, emissions, and power losses and improving the voltage stability related to electric power networks (EPNs). The GTA is inspired by gorillas’ social habits, which include migration to a strange region, migration toward a specified spot, traveling to other gorillas, competing for adult females, and escorting the silverback. The developed GTA is tested with and without the inclusion of the Thyristor-Controlled Series Capacitor (TCSC) devices in the system. The proposed GTA is applied on a practical Egyptian West Delta-EPN (WD-EPN) and the standard IEEE 57-bus EPN and with and without the inclusion of the TCSC devices to appraise the GTA algorithm’s performance in the OPFI. In addition, the proposed GTA is applied on a large-scale IEEE 118 bus system with higher outperformance compared to particle swarm optimization. The results illustrate that the fuel costs, emissions, voltage stability, and power losses are reduced for the standard IEEE 57-bus EPN with and without TCSC devices by a percentage of (18.847% and 18.818%), (59% and 58.97%), (13.405% and 11.507%), and (64.337% and 65.178%), respectively, while fuel costs, emissions, voltage stability, and power losses are reduced for WD-EPN with and without TCSC devices by a percentage of (8.547%, 8.565%), (13.641%, 13.6%), and (61.949%, 61.954%), respectively. A comparison study is conducted to demonstrate the GTA’s effectiveness when compared with other recently developed algorithms such as improved Salp Swarm Algorithm, quasi-reflection jellyfish search, Salp Swarm Algorithm, improved heap-based algorithm, bat search algorithm, social network search algorithm, electromagnetic field optimization, and other well-known algorithms as well. According to the comparison with these algorithms, the GTA demonstrates the best results among the attained results.

Metaheuristic-based Near-Optimal Fractional Order PI Controller for On-grid Fuel Cell Dynamic Performance Enhancement

Fractional-order PI (FOPI) is considered a possible efficient alternative controller to classical PI controller for fuel cell (FC) on-grid systems applications. This paper aims to enhance the dynamic performance of grid-connected FC systems while employing different metaheuristic optimization techniques to FOPI controllers and consequently improve the power quality and stability of the system. The proposed metaheuristic techniques are modified flower pollination algorithm (MFPA), harmony search (HS), and electromagnetic field optimization (EFO). In the study, two FOPI-based regulators are suggested for controlling the current and the power flow between the FC and the network. For this purpose, the inverter's pulse width modulation (PWM) regulation is performed while integrating FC systems into the network. The three metaheuristic optimizers are used for the optimal gain scheduling of the FOPI controller parameters while minimizing the absolute value of the error as the cost function. To demonstrate how efficient the FOPI controllers are, fair comparisons for the on-grid FC-based system performance while proposing the different optimized advanced FOPI and conventional PI controllers are analyzed. According to the simulation results, the most efficient metaheuristic technique is identified based on an impartial defined criterion regarding the dynamic behaviors’ steady-state error, overshoot besides both setting and execution time. Through the suggested criterion, the EFO is considered the best optimization approach. Through the numerical analysis, the power quality assessment of the optimally controlled overall FC on-grid systems is illustrated through voltage sag/swell studies.

Digital image thresholding by using a lateral inhibition 2D histogram and a Mutated Electromagnetic Field Optimization

Digital image thresholding by using a lateral inhibition 2D histogram and a Mutated Electromagnetic Field Optimization

Optimal Values of Unknown Parameters of Polymer Electrolyte Membrane Fuel Cells Using Improved Chaotic Electromagnetic Field Optimization

In this article, the improved chaotic electromagnetic field optimization (ICEFO) algorithm is adopted to generate the optimal values of unknown parameters of fuel cells (FCs). The mathematical model of polymer electrolyte membrane FC (PEMFC) considers nonlinear and complex optimization problems with different control variables. The sum of squared error between the measured and computed stack voltages is considered as the main objective function. The performance of the ICEFO algorithm is tested on three PEMFC stacks. Moreover, sensitivity and statistical measures are presented to confirm the reliability and accuracy of ICEFO. In addition, the effect of changing the cell temperature and reactants pressures is studied for more validation of ICEFO. Furthermore, the results obtained by ICEFO are competitive compared with other optimization methods. These results confirm the effectiveness of ICEFO in solving the optimization problem of PEMFC parameter estimation. Finally, based on the optimization results a Simulink model for PEMFC is developed to examine the dynamic performance of the PEMFCs.

Real power loss reduction by Enhanced Electromagnetic Field and Lightning Process Optimization algorithms

In this research paper Enhanced Electromagnetic Field Optimization (EEFO) algorithm and Lightning Process Optimization (LPO) algorithm are projected to solve the problem. Real power loss reduction is the main objective of the work. In the algorithm population is alienated into three fields: positive, negative, and neutral. Based on the attraction–repulsion forces among these electromagnetic positive, negative, and neutral fields, a new-fangled electromagnetic particle will be engendered in iterations. In Electromagnetic field optimization algorithm, solution symbolized by Electromagnetic Particle (EMPE) and is made through electromagnets which equal to the total number of variables. Among the neighbor’s every electromagnet will imply the attraction and repulsion which possess’​ positive, negative polarity. Then in this paper Lightning Process Optimization (LPO) algorithm is used to solve optimal reactive power problem. Naturally happening lightning process has been imitated to formulate the algorithm. Test points situated on the cloud surface and the ground area are considered as solutions. Objective function is computed with all test points and taken as electric field. With reference to the function of head tip elevation, cloud altitude, channel charge subsequent bound of upward leader is interrelated to charge of the channel. With considering voltage stability index EEFO and LPO algorithms are verified in IEEE 30 bus system. Then EEFO and LPO algorithms evaluated in standard IEEE 14, 30, 57, 118 and 300 bus test systems without considering the voltage stability index. Minimization of real power loss with voltage stability index enhancement has been attained and voltage deviation also minimized.

Prediction of the effects of climate change on hydroelectric generation, electricity demand, and emissions of greenhouse gases under climatic scenarios and optimized ANN model

In this study, an attempt is made to manage the gap between energy demand and energy supply by predicting hydropower production, energy demand, and greenhouse gas emissions. The interaction between climatic, hydrological, and socio-economic parameters creates a nonlinear and uncertain relationship. The complexity of this nonlinear relationship necessitate the use of ANN to estimate energy demand. To predict energy demand, ANN model is used along with improved Electromagnetic Field Optimization (IEFO) algorithms. The results show, hydroelectric generation in the near future under RCP2.6, RCP4.5, and RCP8.5 is decreased 10.981 MW, 12.933MW, and 14.765MW and in the far future decreased 21.922 MW, 23.649 MW, and 26.742 MW. The energy demand increases in the near future 513 MW and far future 1168 MW. According to forecasting hydropower generation and energy demand, the gap between the demand-supply will increase. Also, the greenhouse gases emissions is increase due to the increase in fossil fuel consumption.

Direction of arrival estimation method based on quantum electromagnetic field optimization in the impulse noise

In order to resolve direction finding problems in the impulse noise, a direction of arrival (DOA) estimation method is proposed. The proposed DOA estimation method can restrain the impulse noise by using infinite norm exponential kernel co-variance matrix and obtain excellent performance via the maximum-likelihood (ML) algorithm. In order to obtain the global optimal solutions of this method, a quantum electromagnetic field optimization (QEFO) algorithm is designed. In view of the QEFO algorithm, the proposed method can resolve the difficulties of DOA estimation in the impulse noise. Comparing with some traditional DOA estimation methods, the proposed DOA estimation method shows high superiority and robustness for determining the DOA of independent and coherent sources, which has been verified via the Monte-Carlo experiments of different schemes, especially in the case of snapshot deficiency, low generalized signal to noise ratio (GSNR) and strong impulse noise. Beyond that, the Cramér-Rao bound (CRB) of angle estimation in the impulse noise and the proof of the convergence of the QEFO algorithm are provided in this paper.