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A comparison of swarm-based optimization algorithms in linear antenna array synthesis

JOURNAL OF COMPUTATIONAL ELECTRONICS 2021年第4期20卷 1520-1531页

作者： Durmus, Ali Kurban, Rifat Karakose, Ercan Kayseri Univ Vocat Coll Dept Elect & Energy Kayseri Turkey Kayseri Univ Vocat Coll Dept Comp Technol Kayseri Turkey Kayseri Univ Engn & Architecture & Design Fac Dept Nat Sci Kayseri Turkey

Today, the design of antenna arrays is very important in providing effective and efficient wireless communication. The purpose of antenna array synthesis is to obtain a radiation pattern with a low side lobe level (SLL) at a desired half power beam width in far-field. The amplitude and position values of the array elements can be optimized to obtain a radiation pattern with suppressed SLLs. In this paper, swarm-based metaheuristic algorithms including particle swarm optimization (PSO), artificial bee colony (ABC), mayfly algorithm (MA) and jellyfish search (JS) are compared to determine the optimal design of linear antenna arrays. Extensive experiments are conducted on designing 10-, 16-, 24- and 32-element linear arrays by determining the amplitude and positions. Experiments are repeated 30 times due to the random nature of swarm-based optimizers, and statistical results show that the performance of the novel algorithms, MA and JS, is better than that of the well-known PSO and ABC methods.

关键词： Linear antenna array synthesis Side lobe level Particle swarm optimization Artificial bee colony algorithm mayfly algorithm Jellyfish search algorithm

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Optimal design of renewable sources of PV/wind/FC generation for power system reliability and cost using MA-RBFNN approach

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2021年第7期45卷 10946-10962页

作者： Ramasamy, Krishnakumar Ravichandran, Coimbatore Subramanian Sri Ramakrishna Engn Coll Dept Elect & Elect Engn Coimbatore Tamil Nadu India

This manuscript proposes a hybrid control method for optimal design of hybrid renewable energy sources (RES) like solar photovoltaic (PV)-wind energy and fuel cell (FC) for power system reliability and cost. The proposed hybrid method is the joint execution of mayfly algorithm (MA) and radial basis function neural network (RBFNN) technique and hence, it is named as MA-RBFNN. Here, the MA-RBFNN algorithm is utilized to minimize the cost function and maximize the reliability of the power system. The major objective of the work is "minimizing the cost under several conditions like, wind, PV and FC and also it reliable for the supply of load, with high reliability and less cost." Here, the MA approach is trained with the inputs viz, the previous instantaneous energy of the obtainable sources, the required reliability, and the related target reference source power. The MA-RBFNN control scheme makes the gain parameters to provide the optimal control signal and manage the energy of the RES. Finally, the proposed MA-RBFNN method gives the optimum control of outage components on reliability with system cost. The MA-RBFNN method is executed in matrix laboratory/Simulink site and the performance is analyzed with different existing methods.

关键词： FC load mayfly algorithm PV radial basis function neural network reliability renewable sources replacement cost WT

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MADCR:Mobility Aware Dynamic Clustering-Based Routing Protocol in Internet of Vehicles

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China Communications 2021年第7期18卷 69-85页

作者： Sankar Sennan Somula Ramasubbareddy Sathiyabhama Balasubramaniyam Anand Nayyar Chaker Abdelaziz Kerrache Muhammad Bilal Deparment of of Computer Science and Engineering Sona College of TechnologySalem 636005India Deparment of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabad 500090India. Graduate School Duy Tan UniversityDa Nang 550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang 550000Vietnam Laboratoire d’Informatique et de Mathématiques UniversitéAmar Telidji de LaghouatLaghouatAlgeria. Department of Computer Engineering Hankuk University of Foreign Studies17035Yongin-SiSouth Korea

Internet of Vehicles(IoV)is an evolution of the Internet of Things(IoT)to improve the capabilities of vehicular ad-hoc networks(VANETs)in intelligence transport *** network topology in IoV paradigm is highly *** is one of the promising solutions to maintain the route stability in the dynamic ***,existing algorithms consume a considerable amount of time in the cluster head(CH)selection ***,this study proposes a mobility aware dynamic clustering-based routing(MADCR)protocol in IoV to maximize the lifespan of networks and reduce the end-to-end delay of *** MADCR protocol consists of cluster formation and CH selection processes.A cluster is formed on the basis of Euclidean *** CH is then chosen using the mayfly optimization algorithm(MOA).The CH subsequently receives vehicle data and forwards such data to the Road Side Unit(RSU).The performance of the MADCR protocol is compared with that ofAnt Colony Optimization(ACO),Comprehensive Learning Particle Swarm Optimization(CLPSO),and Clustering algorithm for Internet of Vehicles based on Dragonfly Optimizer(CAVDO).The proposed MADCR protocol decreases the end-toend delay by 5–80 ms and increases the packet delivery ratio by 5%–15%.

关键词： clustering protocol Internet of things Internet of vehicles optimization algorithm mayfly algorithm

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Heart Disease Prediction Using Optimal mayfly Technique with Ensemble Models

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2022年第1期13卷

作者： Prasanna, S. L. Challa, Nagendra Panini VIT AP Univ Amaravati India VIT AP Univ Sch Comp Sci & Engn SCOPE Amaravati India

This paper proposes a methodology consisting of two phases: attributes selection and classification based on the attributes selected. Phase 1 uses the introduced new feature selection algorithm which is the optimal mayfly algorithm (OMA) to solve the feature selection technique problem. mayfly algorithm has derived features of physiological and anatomical relevance, like ST depression, the highest heart rate, cholesterol, chest pain, and heart vessels. In the second phase, the selected attributes use the ensemble classifiers like random subspace, bagging, and boosting. Optimal mayfly algorithm (OMA) with boosting technique had the highest accuracy. Therefore, true disease, false disease, accuracy, and specificity are measured to evaluate the proposed system's efficiency. It has been discovered that the proposed method, which combines feature selection and ensemble techniques performs well, the performance of the optimal mayfly algorithm along with ensemble classifiers of boosting method with a model accuracy of 97.12% which is the highest accuracy value compared to any single model.

关键词： Ensemble algorithms Feature Selection Healthcare Heart Disease mayfly algorithm

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Next-Generation Application-Based Artificial Intelligence in Modeling and Estimation for Ni/n-GaAs/In Schottky Barrier Diode

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PHYSICA STATUS SOLIDI A-APPLICATIONS AND MATERIALS SCIENCE 2023年第6期220卷

作者： Dogan, Hulya Kockanat, Serdar Sivas Cumhuriyet Univ Engn Fac Dept Elect & Elect Engn TR-58140 Sivas Turkiye Sivas Cumhuriyet Univ Dept Elect & Elect Engn Appl Optimizat & DSP Res Lab TR-58140 Sivas Turkiye

Herein, for Ni/n-GaAs/In Schottky barrier diode, experimental measurement, modeling, data generation from the model, and parameter estimation processes are simultaneously carried out. In the experimental step, Ni/n-GaAs/In Schottky barrier diodes are fabricated and annealed from the temperature of 200 degrees C up to 600 degrees C with 100 degrees C steps. Current values are recorded by applying voltage to the diode contacts from -1 V up to 0.5 V. In the modeling step, 1503 experimental current-voltage data are used for 19 different regression models. For Adaptive Neuro Fuzzy System (ANFIS), when root mean square error, mean square error, mean absolute error, and coefficient of determination are calculated 6.0341e-07, 3.6410e-13, 2.3873e-07, and 0.9999 for training, they are obtained 5.8904e-07, 3.4697e-13, 2.3083e-07, and 0.9999 for testing. In the estimation step, the values of electrical parameters are estimated by using mayfly algorithm. Estimations are performed for all annealing temperatures. In addition, current-voltage data for the annealing temperature of 350 degrees C are produced by the ANFIS model. Thus, a new-generation artificial intelligence application, that includes measurement, modeling, and estimation for the Ni/n-GaAs/In Schottky barrier diode with varying annealing temperatures, is realized and a new perspective is provided to researchers and practitioners.

关键词： mayfly algorithm parameter estimation regression schottky barrier diodes

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The Mixture of Probability Distribution Functions for Wind and Photovoltaic Power Systems Using a Metaheuristic Method

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PROCESSES 2022年第11期10卷 2446-2446页

作者： Khamees, Amr Khaled Abdelaziz, Almoataz Y. Eskaros, Makram R. Attia, Mahmoud A. Badr, Ahmed O. Ain Shams Univ Fac Engn Engn Phys & Math Dept Cairo 11517 Egypt Future Univ Egypt Fac Engn & Technol Cairo 2430 Egypt Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo 11517 Egypt

The rising use of renewable energy sources, particularly those that are weather-dependent like wind and solar energy, has increased the uncertainty of supply in these power systems. In order to obtain considerably more accurate results in the analysis of power systems, such as in the planning and operation, it is necessary to tackle the stochastic nature of these sources. Operators require adequate techniques and procedures to mitigate the negative consequences of the stochastic behavior of renewable energy generators. Thus, this paper presents a modification of the original probability distribution functions (PDFs) where the original PDFs are insufficient for wind speed and solar irradiance modeling because they have a significant error between the real data frequency distribution and the estimated distribution curve. This modification is using a mixture of probability distributions, which can improve the fitting of data and reduce this error. The main aim of this paper is to model wind speed and solar irradiance behaviors using a two-component and a three-component mixture of PDFs generated from the integration of the original Weibull, Lognormal, Gamma, and Inverse-Gaussian PDFs. Three statistical errors are used to test the efficiency of the proposed original and mixture PDFs, which are the root mean square error (RMSE), the coefficient of correlation (R-2), and the Chi-square error (X-2). The results show that the mixture of PDFs gives better fitting criteria for wind speed and solar irradiance frequency distributions than the original PDFs. The parameters of the original and the mixture of PDFs are calculated using the innovative metaheuristic mayfly algorithm (MA). The three-component mixture of PDFs lowered the RMSE by about 73% and was 17% more than the best original and the two-component mixture distributions.

关键词： probability distribution functions mixture probability distribution functions metaheuristic optimization methods mayfly algorithm statistical error

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High-altitude Inspection Technology of Substation Based on Fusion of Unmanned Aerial Vehicle and Multiple Sensors

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SENSORS AND MATERIALS 2022年第8期34卷 3191-3212页

作者： Yang, Xiaohua Ye, Xiao Cao, Jianing Yan, Rong Guo, Xiaojue Huang, Jisheng Yunnan Power Grid Co Ltd Measurement Ctr Kunming 650051 Yunnan Peoples R China Kunming Univ Sci & Technol Fac Civil Aviat & Aeronaut Kunming 650500 Yunnan Peoples R China Kunming ZhiYuan Measurement & Control Technol Con Kunming 650500 Yunnan Peoples R China Yunnan Power Grid Co Ltd Lincang Power Supply Bur Lincang 677099 Peoples R China

With the continuous development of the power grid industry, the difficulty of substation operation and maintenance is also increasing. Because of its high mobility and low destructiveness, a rotary-wing unmanned aerial vehicle (UAV) can achieve effective detection and avoid the difficulty and poor accuracy of high-altitude inspection of substations. Owing to the ability of a UAV to integrate various sensors, such as visible light imaging sensors, thermal imaging sensors, and self-positioning sensors, it can ensure its own safety and that of the equipment to be tested during the inspection. Thus, in this paper, we study the application of rotary-wing multi- sensor UAVs in the high-altitude inspection of substations. Firstly, we establish a UAV inspection and autonomous detection model involving electromagnetic and other environmental influences. This model considers substation environmental restrictions, UAV physical constraints, and the limitations of sensor detection, and it is combined with a mobile edge computing platform. Secondly, we design an improved path planning algorithm combining the floating algorithm and the rapidly exploring random trees (RRT) algorithm and introduce improved strategies such as the Cauchy mutation strategy and Levy flight strategy to reduce the blindness of the algorithm search. Finally, by conducting simulation experiments and comparing the designed algorithm with traditional algorithms such as the ant colony algorithm, genetic algorithm, and simulated annealing algorithm, we verify the feasibility of the model and algorithm. The results of this study provide practical assistance to promote the automation and intelligence of substation inspection.

关键词： substation inspection multi-sensor fusion UAV path planning RRT algorithm mayfly algorithm

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Parameter Estimation of Fuel Cell Using Chaotic Mayflies Optimization algorithm

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ADVANCED THEORY AND SIMULATIONS 2021年第12期4卷

作者： Gupta, Jyoti Nijhawan, Parag Ganguli, Souvik Thapar Inst Engn & Technol Elect & Instrumentat Engn Dept Bhadson Rd Patiala 147004 Punjab India

Proton exchange membrane fuel cells (PEMFCs) have been known to be a feasible method for sustainable power generation to meet the ever-increasing electricity demand with no greenhouse gases emission. Thus, under varying operating circumstances, the PEMFCs have gained considerable importance as an alternative energy source. The optimal operation of PEMFC needs to be ensured to exploit its advantages to the maximum limit. In this direction, there is an extensive need for parameter extraction of PEMFC. Many researchers have applied evolutionary optimization approaches to estimate the PEMFCs parameters as the precise modeling of these cells are not possible. To optimize unknown parameters of PEMFCs, a metaheuristic algorithm is proposed, that is, chaotic mayfly algorithm (CMA) is being proposed in this manuscript. The model's statistical effects are more aligned with the actual experimental findings, according to an experimental finding. The results so obtained suggest that CMA variants are a valuable and efficient method of estimating the parameters of PEMFCs models. Even the nonparametric tests like the Friedman ranking test, Wilcoxon's rank-sum test, and Mood's median test suggest that the proposed algorithm shows better accuracy of the extracted parameters as compared to the other algorithms namely considered in this work.

关键词： chaotic variants mayfly algorithm modeling nonparametric tests parameter estimation

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Stochastic Modeling for Wind Energy and Multi-Objective Optimal Power Flow by Novel Meta-Heuristic Method

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IEEE ACCESS 2021年 9卷 158353-158366页

作者： Khamees, Amr Khaled Abdelaziz, Almoataz Y. Eskaros, Makram Roshdy Alhelou, Hassan Haes Attia, Mahmoud Abdallah Ain Shams Univ Fac Engn Dept Engn Phys & Math Cairo 11535 Egypt Future Univ Egypt Fac Engn & Technol Cairo 11835 Egypt Tishreen Univ Dept Elect Power Engn Latakia 2230 Syria Ain Shams Univ Fac Engn Dept Elect Power & Machines Cairo 11535 Egypt

Wind energy is considered one of the most important alternative energy sources for generating electricity. But the stochastic nature of wind, leads to use the distribution function to present the wind system. The two-parameter Weibull distribution is often used in the wind speed presentation. The two-parameter Weibull distribution has scale and shape parameters that are important in wind energy applications, thus selecting the optimum method for estimation them is important. The unpredictability in wind speed leads to uncertainty in devolved power which leads to difficult system operation. In this study, two novel artificial intelligence (AI) methods called mayfly algorithm (MA) and Aquila Optimizer (AO) are used for calculating the Weibull distribution parameters. Results are compared with four classical numerical methods called the Maximum likelihood approach, Energy pattern factor method, Graphical method, and Empirical method. The two AI methods prove superiority and robustness for evaluating two-parameter of Weibull distribution as they give lower errors and higher correlation coefficients. Moreover, to prove the accuracy of the MA method in solving the optimal power flow (OPF) problem, single and multi-objective OPF is applied on a standard IEEE-30 bus system to minimize fuel cost, power loss, thermal unit emissions, and voltage security index (VSI), and results are compared with other metaheuristic methods. The results prove the validity and robustness of the MA method in solving the OPF problem. Then, single and multi-objective stochastic optimal power flow (SCOPF) is applied to modified IEEE-30 which contains two wind farms to minimize total generation cost, power loss, thermal unit emission, and VSI. The fuzzy-based Pareto front technique is utilized in multi-objective optimization (MOO) to obtain the best compromise point solution. The objective function of SCOPF considers reserve cost for overestimation and penalty cost for underestimation of wind energy

关键词： Costs Weibull distribution Wind speed Wind energy Load flow Artificial intelligence Optimization Aquila optimizer mayfly algorithm Weibull distribution optimal power flow multi-objective optimization stochastic optimal power flow wind energy

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