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Du, Qianhang, i Honghao Zhu. "Dynamic elite strategy mayfly algorithm". PLOS ONE 17, nr 8 (25.08.2022): e0273155. http://dx.doi.org/10.1371/journal.pone.0273155.
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The mayfly algorithm (MA), as a newly proposed intelligent optimization algorithm, is found that easy to fall into the local optimum and slow convergence speed. To address this, an improved mayfly algorithm based on dynamic elite strategy (DESMA) is proposed in this paper. Specifically, it first determines the specific space near the best mayfly in the current population, and dynamically sets the search radius. Then generating a certain number of elite mayflies within this range. Finally, the best one among the newly generated elite mayflies is selected to replace the best mayfly in the current population when the fitness value of elite mayfly is better than that of the best mayfly. Experimental results on 28 standard benchmark test functions from CEC2013 show that our proposed algorithm outperforms its peers in terms of accuracy speed and stability.
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Zervoudakis, Konstantinos, i Stelios Tsafarakis. "A mayfly optimization algorithm". Computers & Industrial Engineering 145 (lipiec 2020): 106559. http://dx.doi.org/10.1016/j.cie.2020.106559.
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Zhao, Mengling, Xinlu Yang i Xinyu Yin. "An improved mayfly algorithm and its application". AIP Advances 12, nr 10 (1.10.2022): 105320. http://dx.doi.org/10.1063/5.0108278.
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An improved version of the mayfly algorithm called the golden annealing crossover-mutation mayfly algorithm (GSASMA) is proposed to address the low convergence efficiency and insufficient search capability of existing mayfly algorithms. First, the speed of individual mayflies is optimized using a simulated annealing algorithm to improve the update rate. The position of individuals is improved using the golden sine algorithm. Second, the impact of using different crossover and mutation methods in the algorithm is compared, and the optimal strategy is selected from the algorithm. To evaluate the performance of the algorithm, simulation experiments were carried out for 10 different test functions, and the results were compared with those of existing algorithms. The simulation results show that the algorithm developed in this paper converges faster and the solutions obtained are closer to the global optimum. Finally, GSASMA was used to optimize a support vector machine (SVM) that was used to identify the P300 signal for five subjects. The experimental results show that the SVM optimized by the algorithm proposed in this paper has higher recognition accuracy than an extreme learning machine.
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LI, Linfeng, Weidong LIU i Le LI. "Underwater magnetic field measurement error compensation based on improved mayfly algorithm". Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 40, nr 5 (październik 2022): 1004–11. http://dx.doi.org/10.1051/jnwpu/20224051004.
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This paper investigates the magnetic filed interference problem when the ROV equipped with a three-axis magnetometer measures the magnetic field of underwater magnetic targets within a short range, and a magnetic field compensation method based on an improved mayfly algorithm is proposed to improve the measurement accuracy of underwater magnetic field information. Firstly, a compensation model is established based on the installation error of the three-axis magnetometer and the interference magnetic field of the ROV. Then, in view of the problem that the original mayfly algorithm is easy to fall into local optimal and the convergence accuracy is poor, the Tent chaotic sequence and the Levy flight mutation strategy are introduced to improve the original mayfly algorithm. Finally, a series of magnetic field information is obtained through the three-axis magnetometer, and the original mayfly algorithm, particle swarm algorithm and improved mayfly algorithm are used to estimate the compensation parameters. The experimental results show that the improved mayfly algorithm has obtained faster convergence speed and higher compensation accuracy than others.
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Oladimeji, A. I., A. W. Asaju-Gbolagade i K. A. Gbolagade. "A proposed framework for face - iris recognition system using enhanced mayfly algorithm". Nigerian Journal of Technology 41, nr 3 (2.11.2022): 535–41. http://dx.doi.org/10.4314/njt.v41i3.13.
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Fused biometrics systems have proven to solve some problems associated with unimodal systems but also face challenges in various aspects of their implementation such as difficulty in design, user acceptance is quite low, and the performance tradeoff. This framework tends to address some of these implementation challenges by using an enhanced mayfly algorithm, a modification of the existing mayfly algorithm that was recently proposed, as feature selection. Mayfly algorithm combines advantages of particle swarm optimization, genetic algorithm, and firefly algorithm, simulated in different experiments using varied benchmark function on conventional mayfly algorithm all tested to be capable of optimization, but despite its capabilities, some limitations such as slow convergent or premature convergent rate and possible imbalance between exploration and exploitation still remain unresolved, which necessitated enhancement for better performance. This framework will enhance the existing mayfly algorithm by expanding the search space which limited the ability of the conventional mayfly algorithm to be used to solve high-dimensional problem spaces such as feature selection and modify the selection procedure to model the attraction process as a deterministic process, that will be used for the feature selection procedure on fused face –iris recognition system. This will increase the capabilities of the mayfly algorithm and in turn, increase the recognition accuracy, and reduced the false acceptance rate, false rejection rate, and time complexity of the fused face–iris recognition system.
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Zhao, Juan, i Zheng-Ming Gao. "The negative mayfly optimization algorithm". Journal of Physics: Conference Series 1693 (grudzień 2020): 012098. http://dx.doi.org/10.1088/1742-6596/1693/1/012098.
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Gao, Zheng-Ming, Juan Zhao, Su-Ruo Li i Yu-Rong Hu. "The improved mayfly optimization algorithm". Journal of Physics: Conference Series 1684 (listopad 2020): 012077. http://dx.doi.org/10.1088/1742-6596/1684/1/012077.
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Kadry, Seifedine, Venkatesan Rajinikanth, Jamin Koo i Byeong-Gwon Kang. "Image multi-level-thresholding with Mayfly optimization". International Journal of Electrical and Computer Engineering (IJECE) 11, nr 6 (1.12.2021): 5420. http://dx.doi.org/10.11591/ijece.v11i6.pp5420-5429.
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<span>Image thresholding is a well approved pre-processing methodology and enhancing the image information based on a chosen threshold is always preferred. This research implements the mayfly optimization algorithm (MOA) based image multi-level-thresholding on a class of benchmark images of dimension 512x512x1. The MOA is a novel methodology with the algorithm phases, such as; i) Initialization, ii) Exploration with male-mayfly (MM), iii) Exploration with female-mayfly (FM), iv) Offspring generation and, v) Termination. This algorithm implements a strict two-step search procedure, in which every Mayfly is forced to attain the global best solution. The proposed research considers the threshold value from 2 to 5 and the superiority of the result is confirmed by computing the essential Image quality measures (IQM). The performance of MOA is also compared and validated against the other procedures, such as particle-swarm-optimization (PSO), bacterial foraging optimization</span><span>(BFO), </span><span lang="EN-IN">firefly-algorithm</span><span>(FA), bat algorithm (BA), cuckoo search</span><span>(CS) and moth-flame optimization (MFO) and the attained p-value of Wilcoxon rank test confirmed the superiority of the MOA compared with other algorithms considered in this work</span>
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Prasanna, S. L., i Nagendra Panini Challa. "Heart Disease Prediction Using Optimal Mayfly Technique with Ensemble Models". International Journal of Swarm Intelligence Research 13, nr 1 (1.01.2022): 1–22. http://dx.doi.org/10.4018/ijsir.313665.
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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.
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Zhao, Yanpu, Changsheng Huang, Mengjie Zhang i Yang Cui. "AOBLMOA: A Hybrid Biomimetic Optimization Algorithm for Numerical Optimization and Engineering Design Problems". Biomimetics 8, nr 4 (21.08.2023): 381. http://dx.doi.org/10.3390/biomimetics8040381.
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The Mayfly Optimization Algorithm (MOA), as a new biomimetic metaheuristic algorithm with superior algorithm framework and optimization methods, plays a remarkable role in solving optimization problems. However, there are still shortcomings of convergence speed and local optimization in this algorithm. This paper proposes a metaheuristic algorithm for continuous and constrained global optimization problems, which combines the MOA, the Aquila Optimizer (AO), and the opposition-based learning (OBL) strategy, called AOBLMOA, to overcome the shortcomings of the MOA. The proposed algorithm first fuses the high soar with vertical stoop method and the low flight with slow descent attack method in the AO into the position movement process of the male mayfly population in the MOA. Then, it incorporates the contour flight with short glide attack and the walk and grab prey methods in the AO into the positional movement of female mayfly populations in the MOA. Finally, it replaces the gene mutation behavior of offspring mayfly populations in the MOA with the OBL strategy. To verify the optimization ability of the new algorithm, we conduct three sets of experiments. In the first experiment, we apply AOBLMOA to 19 benchmark functions to test whether it is the optimal strategy among multiple combined strategies. In the second experiment, we test AOBLMOA by using 30 CEC2017 numerical optimization problems and compare it with state-of-the-art metaheuristic algorithms. In the third experiment, 10 CEC2020 real-world constrained optimization problems are used to demonstrate the applicability of AOBLMOA to engineering design problems. The experimental results show that the proposed AOBLMOA is effective and superior and is feasible in numerical optimization problems and engineering design problems.
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Wang, Xing, Jeng-Shyang Pan, Qingyong Yang, Lingping Kong, Václav Snášel i Shu-Chuan Chu. "Modified Mayfly Algorithm for UAV Path Planning". Drones 6, nr 5 (23.05.2022): 134. http://dx.doi.org/10.3390/drones6050134.
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The unmanned aerial vehicle (UAV) path planning problem is primarily concerned with avoiding collision with obstacles while determining the best flight path to the target position. This paper first establishes a cost function to transform the UAV route planning issue into an optimization issue that meets the UAV’s feasible path requirements and path safety constraints. Then, this paper introduces a modified Mayfly Algorithm (modMA), which employs an exponent decreasing inertia weight (EDIW) strategy, adaptive Cauchy mutation, and an enhanced crossover operator to effectively search the UAV configuration space and discover the path with the lowest overall cost. Finally, the proposed modMA is evaluated on 26 benchmark functions as well as the UAV route planning problem, and the results demonstrate that it outperforms the other compared algorithms.
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Zhang, Shoujing, Tiantian Hou, Qing Qu, Adam Glowacz, Samar M. Alqhtani, Muhammad Irfan, Grzegorz Królczyk i Zhixiong Li. "An Improved Mayfly Method to Solve Distributed Flexible Job Shop Scheduling Problem under Dual Resource Constraints". Sustainability 14, nr 19 (25.09.2022): 12120. http://dx.doi.org/10.3390/su141912120.
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Aiming at the distributed flexible job shop scheduling problem under dual resource constraints considering the influence of workpiece transportation time between factories and machines, a distributed flexible job shop scheduling problem (DFJSP) model with the optimization goal of minimizing completion time is established, and an improved mayfly algorithm (IMA) is proposed to solve it. Firstly, the mayfly position vector is discrete mapped to make it applicable to the scheduling problem. Secondly, three-layer coding rules of process, worker, and machine is adopted, in which the factory selection is reflected by machine number according to the characteristics of the model, and a hybrid initialization strategy is designed to improve the population quality and diversity. Thirdly, an active time window decoding strategy considering transportation time is designed for the worker–machine idle time window to improve the local optimization performance of the algorithm. In addition, the improved crossover and mutation operators is designed to expand the global search range of the algorithm. Finally, through simulation experiments, the results of various algorithms are compared to verify the effectiveness of the proposed algorithm for isomorphism and isomerism factories instances.
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Zhao, Juan, i Zheng-Ming Gao. "The improved mayfly optimization algorithm with Chebyshev map". Journal of Physics: Conference Series 1684 (listopad 2020): 012075. http://dx.doi.org/10.1088/1742-6596/1684/1/012075.
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Prasanth, Vidhya, M. Ramachandran i Kurinjimalar Ramu. "A Study on Mayfly Algorithm and Its Recent Developments". Data Analytics and Artificial Intelligence 2, nr 2 (1.08.2022): 109–16. http://dx.doi.org/10.46632/daai/2/2/6.
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It is to define its relationship with the partners during the formation and registration process of S Company Is a legal document prepared and also refers to the minute of the angle corresponding to the MOA 360 minute mark. Each minute represents 1/60 of a degree, just like the minutes of an hour. When shooting, even a small angle can cause you to miss the mark, so it is important to adjust your MOA to a precise angle or fine for a minute. Stands for Memorandum of Association, which refers to articles of association. They help protect and build your business and help establish the company's identity, work ethic and goals. The MOA's first duty is to obtain the patient's personal information before proceeding with the medical journey. Once the MOA collects the patient's information, he will begin transferring the patient to the doctor's office. At this point, the doctor may begin to perform medical procedures. Memorandum of Association (MOA) with its partners defines a company relationship. Is a Memorandum of Association (MOA) To define its relationship with partners Formation of limited liability company And is a legal document prepared during the registration process. The Military Operation Area (MOA) is a Class A aircraft designated to distinguish or differentiate certain hazardous military operations from IFR traffic and to identify VFR traffic carrying these operations. A company is also involved in a business or industrial organization is In order to operate a law firm Created by a group of individuals. They vary between private and public companies. Both have different ownership structures, Terms and conditions include financial statement requirements. The document containing Rules governing the internal management of a company and the regulations are called the article of the association. Select the document type as the consolidation document and select the year the attachment was filed. Pay the fee and request a certified copy. Memorandum of Association (MoA) Memorandum of Association articles there are the following subcategories: This subdivision refers to the name of the company. Company name should not be synonymous with any existing company. Also, if it is a private company, the last word should be the private company.
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Li, Linfeng, Weidong Liu, Le Li, Huifeng Jiao, Junqi Qu i Gongwu Sun. "Compensation of Optical Pump Magnetometer Using the Improved Mayfly Optimization Algorithm". Journal of Marine Science and Engineering 10, nr 12 (12.12.2022): 1982. http://dx.doi.org/10.3390/jmse10121982.
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In order to solve the problem that the cesium optical pump magnetometer is disturbed by the carrier’s interference magnetic field during magnetic field anomaly detection, an interference magnetic field compensation method based on an improved mayfly optimization algorithm (IMOA) was proposed in this paper. First, by combining the measurement results of the attitude sensor with the geomagnetic inclination and magnetic declination in the locality, the measurement results of the optical pump magnetometer can be decomposed into the component values under the three axes of the carrier coordinate system. A compensation model including the carrier interference magnetic field was established. Then, considering the poor global search performance that existed in the mayfly optimization algorithm (MOA), an elite chaotic reverse learning strategy and Levy mutation strategy were introduced to improve the MOA. The compensation performance of the IMOA was estimated with a series of field experiments and compared with the stretching particle swarm optimization algorithm. The experiment results indicated that these two methods can effectively compensate the magnetometer’s measurement values, and that the IMOA method more easily jumps out of the local optimum, and has higher compensation accuracy.
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Gao, Zheng-Ming, Juan Zhao, Su-Ruo Li i Yu-Rong Hu. "The improved mayfly optimization algorithm with opposition based learning rules". Journal of Physics: Conference Series 1693 (grudzień 2020): 012117. http://dx.doi.org/10.1088/1742-6596/1693/1/012117.
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Barhanpurkar, Atharva, Deepak Hujare, Omkar Kulkarni i Abhijeet Birari. "Optimization of Flywheel for Reciprocating Air Compressor using Mayfly Algorithm". International Journal of Engineering Trends and Technology 71, nr 8 (15.08.2023): 191–200. http://dx.doi.org/10.14445/22315381/ijett-v71i8p217.
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Wang, Ji-Quan, Hong-Yu Zhang, Hao-Hao Song, Pan-Li Zhang i Jin-Ling Bei. "Prediction of Pork Supply Based on Improved Mayfly Optimization Algorithm and BP Neural Network". Sustainability 14, nr 24 (9.12.2022): 16559. http://dx.doi.org/10.3390/su142416559.
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Focusing on the issues of slow convergence speed and the ease of falling into a local optimum when optimizing the weights and thresholds of a back-propagation artificial neural network (BPANN) by the gradient method, a prediction method for pork supply based on an improved mayfly optimization algorithm (MOA) and BPANN is proposed. Firstly, in order to improve the performance of MOA, an improved mayfly optimization algorithm with an adaptive visibility coefficient (AVC-IMOA) is introduced. Secondly, AVC-IMOA is used to optimize the weights and thresholds of a BPANN (AVC-IMOA_BP). Thirdly, the trained BPANN and the statistical data are adopted to predict the pork supply in Heilongjiang Province from 2000 to 2020. Finally, to demonstrate the effectiveness of the proposed method for predicting pork supply, the pork supply in Heilongjiang Province was predicted by using AVC-IMOA_BP, a BPANN based on the gradient descent method and a BPANN based on a mixed-strategy whale optimization algorithm (MSWOA_BP), a BPANN based on an artificial bee colony algorithm (ABC_BP) and a BPANN based on a firefly algorithm and sparrow search algorithm (FASSA_BP) in the literature. The results show that the prediction accuracy of the proposed method based on AVC-IMOA and a BPANN is obviously better than those of MSWOA_BP, ABC_BP and FASSA_BP, thus verifying the superior performance of AVC-IMOA_BP.
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Liu, Ximu, Mi Zhao, Zihan Wei i Min Lu. "Economic Optimal Scheduling of Wind–Photovoltaic-Storage with Electric Vehicle Microgrid Based on Quantum Mayfly Algorithm". Applied Sciences 12, nr 17 (31.08.2022): 8778. http://dx.doi.org/10.3390/app12178778.
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The effectiveness of energy management systems is a great concern for wind–photovoltaic-storage electric vehicle systems, which coordinate operation optimization and flexible scheduling with the power grid. In order to save system operation cost and reduce the energy waste caused by wind and light abandonment, a time-sharing scheduling strategy based on the state of charge (SOC) and flexible equipment is proposed, and a quantum mayfly algorithm (QMA) is innovatively designed to implement the strategy. Firstly, a scheduling strategy is produced according to the SOC of the battery and electric vehicle (EV), as well as the output power of wind–photovoltaic generation. In addition, the minimum objective function of the comprehensive operation cost is established by considering the cost of each unit’s operation and electricity market sale price. Secondly, QMA is creatively developed, including its optimization rule, whose performance evaluation is further carried out by comparisons with other typical bionics algorithms. The advantages of QMA in solving the low-power multivariable functions established in this paper are verified in the optimization results. Finally, using the empirical value of the power generation and loads collected in enterprise as the initial data, the mayfly algorithm (MA) and QMA are executed in MATLAB to solve the objective function. The scheduling results show that the time-sharing scheduling strategy can reduce the system’s cost by 60%, and the method decreases energy waste compared with ordinary scheduling methods, especially when using QMA to solve the function
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Bhattacharyya, Trinav, Bitanu Chatterjee, Pawan Kumar Singh, Jin Hee Yoon, Zong Woo Geem i Ram Sarkar. "Mayfly in Harmony: A New Hybrid Meta-Heuristic Feature Selection Algorithm". IEEE Access 8 (2020): 195929–45. http://dx.doi.org/10.1109/access.2020.3031718.
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Hu, Zhixiang, Huiyu Zhu, Lei Huang i Cheng Cheng. "Damage Identification Method and Uncertainty Analysis of Beam Structures Based on SVM and Swarm Intelligence Algorithm". Buildings 12, nr 11 (11.11.2022): 1950. http://dx.doi.org/10.3390/buildings12111950.
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A two-stage damage identification method for beam structures based on support vector machine and swarm intelligence optimization algorithms is proposed. First, the frequencies and mode shapes of the beam structure are obtained using the smooth orthogonal decomposition method, and the normalized modal curvature is calculated as the input of a pre-trained support vector machine to determine the damage location. Then, the stiffness loss at the damaged location of the structure is calculated using swarm intelligence algorithms. The fitness function is the sum of the residual squares of the frequencies of the damaged structure identified by the smooth orthogonal decomposition method and the frequencies calculated for each iteration of the intelligent optimization algorithm. Numerical examples of a damaged simply supported beam structure are used to verify the damage identification performance of the two-stage method. The accuracy of the support vector machine model under different damage degrees and noise levels is studied using the Monte-Carlo method, and an uncertainty of the damage degree prediction value is studied by comparing the particle swarm optimization algorithm, moth-fire algorithm, and mayfly algorithm.
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Kyomugisha, Rebeccah, Christopher Maina Muriithi i George Nyauma Nyakoe. "Performance of Various Voltage Stability Indices in a Stochastic Multiobjective Optimal Power Flow Using Mayfly Algorithm". Journal of Electrical and Computer Engineering 2022 (29.04.2022): 1–22. http://dx.doi.org/10.1155/2022/7456333.
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The performance of voltage stability indices in the multiobjective optimal power flow of modern power systems is presented in this work. Six indices: the Voltage Collapse Proximity Index (VCPI), Line Voltage Stability Index (LVSI), Line Stability Index (Lmn), Fast Voltage Stability Index (FVSI), Line Stability Factor (LQP), and Novel Line Stability Index (NLSI) were considered as case studies on a modified IEEE 30-bus consisting of thermal, wind, solar and hybrid wind-hydro generators. A multiobjective evaluation using the multiobjective mayfly algorithm (MOMA) was performed in two operational scenarios: normal and contingency conditions, using the MATLAB–MATPOWER toolbox. Fuzzy Decision-Making technique was used to determine the best compromise solutions for each Pareto front. To evaluate the computational efficiency of the case studies, a preference selection index was used. The results indicate that VCPI and NLSI yielded the best-optimized system performance in minimizing generation costs, transmission loss reduction, and simulation time for normal and contingency conditions. The best-case studies also promoted the most scheduled reactive power generation from renewable energy sources (RES). On average, the VCPI index contributed the highest penetration level from RES (13.40%), while the Lmn index had the lowest. Overall, VCPI and Lmn index provided the best and worst average performance in both operating scenarios, respectively. Also, the MOMA algorithm demonstrated superior performance against the multiobjective harris hawks algorithm (MHHO), multiobjective Jaya algorithm (MOJAYA), multiobjective particle swarm algorithm (MOPSO), and nondominated sorting genetic algorithm III (NSGA-III) algorithms. In all, the proposed approach yields the lowest system cost and loss compared to other methods.
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Yousaf, Muhammad Zain, Ali Raza, Ghulam Abbas, Nasim Ullah, Ahmad Aziz Al-Ahmadi, Abdul Rehman Yasin i Mohsin Jamil. "MTDC Grids: A Metaheuristic Solution for Nonlinear Control". Energies 15, nr 12 (9.06.2022): 4263. http://dx.doi.org/10.3390/en15124263.
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This scientific paper aims to increase the voltage source converter (VSC) control efficiency in a multi-terminal high voltage direct current (MTDC) network during dynamic operations. In the proposed study, the Mayfly algorithm (MA) is used to modify the control parameters of VSC stations. Traditional strategies that modify VSC control settings using approximate linear models fail to produce optimal results because VSCs are nonlinear characteristics of the MTDC system. Particle swarm optimization (PSO) may produce optimal outcomes, but it is prone to becoming stuck in a local optimum. To modify the proportional-integral (P.I.) control parameters of the VSC station, the Mayfly algorithm, a modified form of PSO, is used. The suggested algorithm’s objective function simultaneously optimizes both the outer and inner control layers. A four-terminal MTDC test system is developed in PSCAD / EMTDC to assess the benefits of the proposed algorithm. For VSCs, a comparison of classical, PSO, and proposed MA-based tuned parameters is carried out. The integral of time multiplied by absolute error (ITAE) criterion is used to compare the performance of classical, PSO, and a proposed algorithm for VSC controller parameters/gains. With an ITAE value of 6.8521 × 10−6, the MA-based proposed algorithm computes the optimal values and outperforms its predecessor to adjust the VSCs controller gains. For (i) wind farm power variation, (ii) AC grid load demand variation, and (iii) ultimate permanent VSC disconnection, steady-state and dynamic performances are evaluated. According to the results, the proposed algorithm based MTDC system performs well during transients.
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Mukase, Sandrine, i Kewen Xia. "Multi-Objective Optimization with Mayfly Algorithm for Periodic Charging in Wireless Rechargeable Sensor Networks". World Electric Vehicle Journal 13, nr 7 (1.07.2022): 120. http://dx.doi.org/10.3390/wevj13070120.
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Wireless energy transfer (WET) is a revolutionary method that has the power to tackle the energy and longevity challenges in wireless sensor networks (WSN). This paper uses a mobile charger (MC) to discover the procedure of WET based on a wireless sensor network (WSN) for a periodic charging technique to maintain the network operational. The goal of this work is to lower overall system energy consumption and total distance traveled while increasing the mobile charger device vacation time ratio. Based on an analysis of total energy consumption, a new metaheuristic called mayfly algorithm (MA) is used to achieve energy savings. Instead of charging all nodes at the same time in each cycle, in our strategy, the mobile charger charges only energy-hungry nodes due to their levels of energy. In this strategy, when the first node reaches the calculated minimum energy, it notifies the base station (BS), which computes all nodes that fall under threshold energy and sends the MC to charge all of them to the maximum energy level in the same cycle. Mathematical results show that the mayfly algorithm can considerably decrease the charging device’s total energy consumption and distance traveled while maintaining performance because it can keep the network operational with less complexity than other schemes.
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Hu, Aihua, Zhongliang Deng, Hui Yang, Yao Zhang, Yuhui Gao i Di Zhao. "An Optimal Geometry Configuration Algorithm of Hybrid Semi-Passive Location System Based on Mayfly Optimization Algorithm". Sensors 21, nr 22 (11.11.2021): 7484. http://dx.doi.org/10.3390/s21227484.
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In view of the demand of location awareness in a special complex environment, for an unmanned aerial vehicle (UAV) airborne multi base-station semi-passive positioning system, the hybrid positioning solutions and optimized site layout in the positioning system can effectively improve the positioning accuracy for a specific region. In this paper, the geometric dilution of precision (GDOP) formula of a time difference of arrival (TDOA) and angles of arrival (AOA) hybrid location algorithm is deduced. Mayfly optimization algorithm (MOA) which is a new swarm intelligence optimization algorithm is introduced, and a method to find the optimal station of the UAV airborne multiple base station’s semi-passive positioning system using MOA is proposed. The simulation and analysis of the optimization of the different number of base stations, compared with other station layout methods, such as particle swarm optimization (PSO), genetic algorithm (GA), and artificial bee colony (ABC) algorithm. MOA is less likely to fall into local optimum, and the error of regional target positioning is reduced. By simulating the deployment of four base stations and five base stations in various situations, MOA can achieve a better deployment effect. The dynamic station configuration capability of the multi-station semi-passive positioning system has been improved with the UAV.
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Mo, Shixun, Qintao Ye, Kunping Jiang, Xiaofeng Mo i Gengyu Shen. "An improved MPPT method for photovoltaic systems based on mayfly optimization algorithm". Energy Reports 8 (sierpień 2022): 141–50. http://dx.doi.org/10.1016/j.egyr.2022.02.160.
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Boopathi, Dhanasekaran, Kaliannan Jagatheesan, Baskaran Anand, Sourav Samanta i Nilanjan Dey. "Frequency Regulation of Interlinked Microgrid System Using Mayfly Algorithm-Based PID Controller". Sustainability 15, nr 11 (30.05.2023): 8829. http://dx.doi.org/10.3390/su15118829.
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The primary goal of this article is to design and implement a secondary controller with which to control the system frequency in a networked microgrid system. The proposed power system comprises of Renewable energy sources (RESs), energy-storing units (ESUs), and synchronous generator. RESs include photovoltaic (PV) and wind turbine generator (WTG) units. The ESU is composed of a flywheel and a battery. Because renewable energy sources are not constant in nature, their values fluctuate from time to time, causing an effect on system frequency and power flow variation in the tie line. The nonlinear output from the RESs is balanced with the support of ESUs. In order to address this situation, a proportional integral derivative (PID) controller based on the Mayfly algorithm (MA) is proposed and built. Comparing the responses of controllers based on the genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) technique-optimized to demonstrate the superiority of the MA-tuned controller.. The results of the validation comparisons reveal that the implemented MA-PID controller delivers and is capable of regulating system frequency under various load demand changes and renewable energy sources. A robustness analysis test was also performed in order to determine the effectiveness of the suggested optimization technique (1%, 2%, 5%, and 10%) step load perturbation (SLP) with ±25% and ±50% variation from the nominal governor and reheater time constant).
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Olaniyan, Olatayo Moses, Ayobami Taiwo Olusesi, Bolaji Abigail Omodunbi, Wajeed Bolanle Wahab, Olusogo Julius Adetunji i Bamidele Musiliu Olukoya. "A Data Security Model for Mobile Ad Hoc Network Using Linear Function Mayfly Advanced Encryption Standard". International Journal of Emerging Technology and Advanced Engineering 13, nr 3 (1.03.2023): 101–10. http://dx.doi.org/10.46338/ijetae0323_10.
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Mobile Ad Hoc network (MANET) is a connection of mobile nodes that are joined together to communicate and share information using a wireless link.Some of the MANET in use include mobile smart phones, laptops, personal digital assistant (PDAs), among others.However, MANET has been known for the major challenge of being vulnerable to malicious attacks within the network. One of the techniques which have been used by several research works is the cryptographic approach using advanced encryption technique (AES). AES has been found suitable in the MANET domain because it does not take much space in mobile nodes which are known for their limited space resources. But one of the challenges facing AES which has not been given much attention is the optimal generation of its secret keys. So, therefore, this research work presents a symmetric cryptography technique by developing a model for the optimal generation of secret keys in AES using the linear function mayfly AES (LFM-AES) algorithm. The developed model was simulated in MATLAB 2020 programming environment. LFM-AES was compared with mayfly-AES, particle swarm optimization AES (PSO-AES) using encryption time, computational time, encryption throughput, and mean square error. The simulation results showed that LFM-AES has lower encryption, computational, mean square error, and higher encryption throughput. Keywords-- MANET, Data Security, Key Management, LFM-AES, Mayfly-AES, PSO-AES, AES
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Lim, Ming K., Yan Li, Chao Wang i Ming-Lang Tseng. "Prediction of cold chain logistics temperature using a novel hybrid model based on the mayfly algorithm and extreme learning machine". Industrial Management & Data Systems 122, nr 3 (8.03.2022): 819–40. http://dx.doi.org/10.1108/imds-10-2021-0607.
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PurposeThe transportation of fresh food requires cold chain logistics to maintain a low-temperature environment, which can reduce food waste and ensure product safety. Therefore, temperature control is a major challenge that cold chain logistics face.Design/methodology/approachThis research proposes a prediction model of refrigerated truck temperature and air conditioner status (air speed and air temperature) based on hybrid mayfly algorithm (MA) and extreme learning machine (ELM). To prove the effectiveness of the proposed method, the mayfly algorithm–extreme learning machine (MA-ELM) is compared with the traditional ELM and the ELM optimized by classical biological-inspired algorithms, including the genetic algorithm (GA) and particle swarm optimization (PSO). The assessment is conducted through two experiments, including temperature prediction and air conditioner status prediction, based on a case study.FindingsThe prediction method is evaluated by five evaluation indicators, including the mean relative error (MRE), mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE) and coefficient of determination (R2). It can be concluded that the biological algorithm, especially the MA, can improve the prediction accuracy. This result clearly proves the effectiveness of the proposed hybrid prediction model in revealing the nonlinear patterns of the cold chain logistics temperature.Research limitations/implicationsThe case study illustrates the effectiveness of the proposed temperature prediction method, which helps to keep the product fresh. Even though the performance of MA is better than GA and PSO, the MA has the disadvantage of premature convergence. In the future, the modified MA can be designed to improve the performance of MA-ELM.Originality/valueIn prior studies, many scholars have conducted related research on the subject of temperature monitoring. However, this monitoring method can only identify temperature deviations that have occurred that harmed fresh food. As a countermeasure, research on the temperature prediction of cold chain logistics that can actively identify temperature changes has become the focus. Once a temperature deviation is predicted, temperature control measures can be taken in time to resolve the risk.
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Qassem, Deema Yahya, i Najla Akram Al_saati. "A Solution to the Next Release Problem by Swarm Intelligence". Technium: Romanian Journal of Applied Sciences and Technology 12 (22.08.2023): 58–64. http://dx.doi.org/10.47577/technium.v12i.9439.
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First of all, in this research, we solve the problem of the next release ((NRP) (Next Release Problem)), which is classified as a multi-objective difficult problem (NP_ hard problem) using swarm intelligence, since the programs are spread in all areas of our life and process The development on it is constantly ongoing and the selection of the optimal requirements to satisfy customers for the following versions is a very important process, as the requirements that have been dealt with are complicated due to interdependence and other limitations. Therefore, we will highlight it in our research to solve it, as the problem of the next release (NRP) is defined as a multi-objective improvement problem with two conflicting goals, which are customer satisfaction and development cost, and since it is a multi-objective problem, we chose swarm intelligence to solve it, where we solved This problem using the Multi_objective Mayfly Algorithm is derived from the behavior of the swarms of the Mayfly in nature.
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Lohat, Savita, Sheilza Jain i Rajender Kumar. "Improved Delay and PDR For Iov-Fog Nets Using Fractional Mayfly Optimization Algorithm". ITM Web of Conferences 54 (2023): 02008. http://dx.doi.org/10.1051/itmconf/20235402008.
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The integration of the automobile industry with communication technology has led to the concept of the Internet of Vehicles (IoV). It is a self-organized network that consists of vehicles and RSUs and employs Infrastructure-to-Vehicle (I2V) and Vehicle-to-Vehicle (V2V) data transmission mechanisms. The IoV system uses an efficient service message transmission protocol, the Fractional Mayfly algorithm (FMA), for reliable broadcasting of service information. Experimental results indicate that the FMA-based scheduling method is superior in terms of delay and PDR, particularly for 100 and 150 vehicles. The Fog Computing approach is also used for communication and data processing in the IoV system.
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Yildiz, Betül Sultan, Sujin Bureerat, Natee Panagant, Pranav Mehta i Ali Riza Yildiz. "Reptile search algorithm and kriging surrogate model for structural design optimization with natural frequency constraints". Materials Testing 64, nr 10 (1.10.2022): 1504–11. http://dx.doi.org/10.1515/mt-2022-0048.
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Abstract This study explores the use of a recent metaheuristic algorithm called a reptile search algorithm (RSA) to handle engineering design optimization problems. It is the first application of the RSA to engineering design problems in literature. The RSA optimizer is first applied to the design of a bolted rim, which is constrained optimization. The developed algorithm is then used to solve the optimization problem of a vehicle suspension arm, which aims to solve the weight reduction under natural frequency constraints. As function evaluations are achieved by finite element analysis, the Kriging surrogate model is integrated into the RSA algorithm. It is revealed that the optimum result gives a 13% weight reduction compared to the original structure. This study shows that RSA is an efficient metaheuristic as other metaheuristics such as the mayfly optimization algorithm, battle royale optimization algorithm, multi-level cross-entropy optimizer, and red fox optimization algorithm.
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Liu, Yuhu, Yi Chai, Bowen Liu i Yiming Wang. "Bearing Fault Diagnosis Based on Energy Spectrum Statistics and Modified Mayfly Optimization Algorithm". Sensors 21, nr 6 (23.03.2021): 2245. http://dx.doi.org/10.3390/s21062245.
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This study proposes a novel resonance demodulation frequency band selection method named the initial center frequency-guided filter (ICFGF) to diagnose the bearing fault. The proposed technology has a better performance on resisting the interference from the random impulses. More explicitly, the ICFGF can be summarized as two steps. In the first step, a variance statistic index is applied to evaluate the energy spectrum distribution, which can adaptively determine the center frequency of the fault impulse and suppress the interference from random impulse effectively. In the second step, a modified mayfly optimization algorithm (MMA) is applied to search the optimal resonance demodulation frequency band based on the center frequency from the first step, which has faster convergence. Finally, the filtered signal is processed by the squared envelope spectrum technology. Results of the proposed method for signals from an outer fault bearing and a ball fault bearing indicate that the ICFGF works well to extract bearing fault feature. Furthermore, compared with some other methods, including fast kurtogram, ensemble empirical mode decomposition, and conditional variance-based selector technology, the ICFGF can extract the fault characteristic more accurately.
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Hu, Hongming, Huawang Li, Guang Liang, Lulu Zhao, Jiashuo Yang i Xiaoli Wei. "Phase-Only Pattern Synthesis for Spaceborne Array Antenna Based on Improved Mayfly Optimization Algorithm". Electronics 12, nr 4 (9.02.2023): 895. http://dx.doi.org/10.3390/electronics12040895.
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A new optimization algorithm—Improved Mayfly Optimization Algorithm (IMOA)—is proposed in this paper to fulfill the low sidelobe level (SLL) design requirements of the spaceborne array antenna. MOA is a new heuristic algorithm inspired by the flying behavior and mating process of mayflies. It has a unique speed updating system with great convergence, strong stability, fast solution speed, and high precision. Based on the MOA, IMOA not only introduces the adaptive inertial weight factor to enhance the search ability, but also uses the Levy flight strategy and the golden sine operator to improve the disadvantage of easily falling into the local optimal solution. Firstly, according to the antenna pattern requirements of high gain and low sidelobe, an optimization problem model is carried out. Then, the IMOA is applied to solve the problem by only controlling the phase under a given secondary amplitude distribution. Simulation results show that IMOA has great advantages in the maximum sidelobe level (MSLL) suppression and convergence speed. Finally, the EM simulations are conducted on the 528-element planar array antenna. The maximum sidelobe level suppression performances in the test are very consistent with the theoretical simulation, which verifies the feasibility and effectiveness of the proposed IMOA.
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Farki, Ali, Zahra Salekshahrezaee, Arash Mohammadi Tofigh, Reza Ghanavati, Behdad Arandian i Amirahmad Chapnevis. "COVID-19 Diagnosis Using Capsule Network and Fuzzy C -Means and Mayfly Optimization Algorithm". BioMed Research International 2021 (19.10.2021): 1–11. http://dx.doi.org/10.1155/2021/2295920.
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The COVID-19 epidemic is spreading day by day. Early diagnosis of this disease is essential to provide effective preventive and therapeutic measures. This process can be used by a computer-aided methodology to improve accuracy. In this study, a new and optimal method has been utilized for the diagnosis of COVID-19. Here, a method based on fuzzy C -ordered means (FCOM) along with an improved version of the enhanced capsule network (ECN) has been proposed for this purpose. The proposed ECN method is improved based on mayfly optimization (MFO) algorithm. The suggested technique is then implemented on the chest X-ray COVID-19 images from publicly available datasets. Simulation results are assessed by considering a comparison with some state-of-the-art methods, including FOMPA, MID, and 4S-DT. The results show that the proposed method with 97.08% accuracy and 97.29% precision provides the highest accuracy and reliability compared with the other studied methods. Moreover, the results show that the proposed method with a 97.1% sensitivity rate has the highest ratio. And finally, the proposed method with a 97.47% F 1 -score rate gives the uppermost value compared to the others.
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Xie, Xiaode, Jiali Zheng, Minyu Feng, Siyi He i Zihan Lin. "Multi-Objective Mayfly Optimization Algorithm Based on Dimensional Swap Variation for RFID Network Planning". IEEE Sensors Journal 22, nr 7 (1.04.2022): 7311–23. http://dx.doi.org/10.1109/jsen.2022.3151932.
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Nagarajan, Karthik, Arul Rajagopalan, S. Angalaeswari, L. Natrayan i Wubishet Degife Mammo. "Combined Economic Emission Dispatch of Microgrid with the Incorporation of Renewable Energy Sources Using Improved Mayfly Optimization Algorithm". Computational Intelligence and Neuroscience 2022 (18.04.2022): 1–22. http://dx.doi.org/10.1155/2022/6461690.
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Electricity can be provided to small-scale communities like commercial areas and villages through microgrid, one of the small-scale, advanced, and independent electricity systems out of the grid. Microgrid is an appropriate choice for specific purposes reducing emission and generation cost and increasing efficiency, reliability, and the utilization of renewable energy sources. The main objective of this paper is to elucidate the combined economic emission dispatch CEED problem in the microgrid to attain optimal generation cost. A combined cost optimization approach is examined to minimize operational cost and emission levels while satisfying the load demand of the microgrid. With this background, the authors proposed a novel improved mayfly algorithm incorporating Levy flight to resolve the combined economic emission dispatch problem encountered in microgrids. The islanded mode microgrid test system considered in this study comprises thermal power, solar-powered, and wind power generating units. The simulation results were considered for 24 hours with varying power demands. The minimization of total cost and emission is attained for four different scenarios. Optimization results obtained for all scenarios using IMA give a comparatively better reduction in system cost than MA and other optimization algorithms considered revealing the efficacy of IMA taken for comparison with the same data. The proposed IMA algorithm can solve the CEED problem in a grid-connected microgrid.
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Tamilmani, G., V. Brindha Devi, T. Sujithra, Francis H. Shajin i P. Rajesh. "Cancer MiRNA biomarker classification based on Improved Generative Adversarial Network optimized with Mayfly Optimization Algorithm". Biomedical Signal Processing and Control 75 (maj 2022): 103545. http://dx.doi.org/10.1016/j.bspc.2022.103545.
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Zghoul, Fadi Nessir, Haneen Alteehi i Ahmad Abuelrub. "A Mayfly-Based Approach for CMOS Inverter Design with Symmetrical Switching". Algorithms 16, nr 5 (30.04.2023): 237. http://dx.doi.org/10.3390/a16050237.
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This paper presents a novel approach to designing a CMOS inverter using the Mayfly Optimization Algorithm (MA). The MA is utilized in this paper to obtain symmetrical switching of the inverter, which is crucial in many digital electronic circuits. The MA method is found to have a fast convergence rate compared to other optimization methods, such as the Symbiotic Organisms Search (SOS), Particle Swarm Optimization (PSO), and Differential Evolution (DE). A total of eight different sets of design parameters and criteria were analyzed in Case I, and the results confirmed compatibility between the MA and Spice techniques. The maximum discrepancy in fall time across all design sets was found to be 2.075711 ns. In Case II, the objective was to create a symmetrical inverter with identical fall and rise times. The difference in fall and rise times was minimized based on Spice simulations, with the maximum difference measuring 0.9784731 ns. In Case III, the CMOS inverter was designed to achieve symmetrical fall and rise times as well as propagation delays. The Spice simulation results demonstrated that symmetry had been successfully achieved, with the minimum difference measuring 0.312893 ns and the maximum difference measuring 1.076540 ns. These Spice simulation results are consistent with the MA results. The results conclude that the MA is a reliable and simple optimization technique and can be used in similar electronic topologies.
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Wei, Donghui, Jinghong Ji, Junlong Fang i Nasser Yousefi. "Evaluation and optimization of PEM Fuel Cell-based CCHP system based on Modified Mayfly Optimization Algorithm". Energy Reports 7 (listopad 2021): 7663–74. http://dx.doi.org/10.1016/j.egyr.2021.10.118.
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Manic, K. Suresh, Venkatesan Rajinikanth, Ali Saud Al-Bimani, David Taniar i Seifedine Kadry. "Framework to Detect Schizophrenia in Brain MRI Slices with Mayfly Algorithm-Selected Deep and Handcrafted Features". Sensors 23, nr 1 (27.12.2022): 280. http://dx.doi.org/10.3390/s23010280.
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Brain abnormality causes severe human problems, and thorough screening is necessary to identify the disease. In clinics, bio-image-supported brain abnormality screening is employed mainly because of its investigative accuracy compared with bio-signal (EEG)-based practice. This research aims to develop a reliable disease screening framework for the automatic identification of schizophrenia (SCZ) conditions from brain MRI slices. This scheme consists following phases: (i) MRI slices collection and pre-processing, (ii) implementation of VGG16 to extract deep features (DF), (iii) collection of handcrafted features (HF), (iv) mayfly algorithm-supported optimal feature selection, (v) serial feature concatenation, and (vi) binary classifier execution and validation. The performance of the proposed scheme was independently tested with DF, HF, and concatenated features (DF+HF), and the achieved outcome of this study verifies that the schizophrenia screening accuracy with DF+HF is superior compared with other methods. During this work, 40 patients’ brain MRI images (20 controlled and 20 SCZ class) were considered for the investigation, and the following accuracies were achieved: DF provided >91%, HF obtained >85%, and DF+HF achieved >95%. Therefore, this framework is clinically significant, and in the future, it can be used to inspect actual patients’ brain MRI slices.
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Azeez, Abdul, i Suraiya Tarannum. "Multi-Objective Mayfly Optimization in Phase Optimization of OFDM". IIUM Engineering Journal 24, nr 1 (4.01.2023): 106–21. http://dx.doi.org/10.31436/iiumej.v24i1.2625.
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Communication systems have been used tremendously in recent years which results in the need for high data transmission rates. Orthogonal Frequency Division Multiplexing (OFDM) provides robust performance in frequency selective fading due to high bandwidth efficiency and inter-symbol interference. Various optimization techniques were applied in existing research to increase the efficiency of OFDM in a communication system. The existing research has a limitation of considering a single objective to improve the efficiency of OFDM and also has a local optima trap. This research proposes a Multi-Objective Mayfly algorithm (MOMF) to consider multi-objective and provides a proper trade-off between exploration and exploitation. The Partial Transmit Sequence (PTS) is applied in the model to test the performance. The FFT sizes and modulation orders are varied to evaluate the performance of the MOMF technique in phase optimization. The MOMF technique effectively increases the performance of the model than other existing optimization techniques. The MOMF technique provides a non-dominated solution to escape from local optima trap. The MOMF model considers PAPR, BER, and SER in MIMO-OFDM system to increase the efficiency of the system. The exploration-exploitation trade-off helps to improve the convergence and overcome local optima trap. The MOMF in OFDM phase optimization was evaluated using BER, SER, and Peak-to-Average Power Ratio (PAPR) metrics. The MOMF method has PAPR of 3.95 dB and PSO-GWO method has 4.92 dB of PAPR. ABSTRAK: Sistem komunikasi telah digunakan secara meluas sejak beberapa tahun ini dan dapatan kajian menunjukkan keperluan pada kadar transmisi data yang tinggi. Pemultipleksan Bahagian Frekuensi Ortogon (OFDM) menyediakan prestasi berkesan dalam pemilihan pemudaran frekuensi berdasarkan keberkesanan lebar jalur tinggi dan gangguan antara-simbol. Pelbagai teknik optimum digunakan pada kajian sebelum ini bagi meningkatkan keberkesanan OFDM dalam sistem komunikasi. Kajian tersebut mempunyai kekurangan dalam memilih satu objektif bagi membaiki keberkesanan OFDM dan juga mempunyai perangkap optima setempat. Kajian ini mencadangkan algoritma Mayfly Objektif-Pelbagai (MOMF) bagi memilih objektif-pelbagai dan menyediakan keseimbangan yang wajar antara eksplorasi dan eksploitasi. Urutan Pancar Separa (PTS) telah digunakan dalam model ini bagi menguji prestasi. Saiz FFT dan turutan modulasi dipelbagaikan bagi menguji keberkesanan teknik MOMF pada fasa pengoptimuman. Teknik MOMF dengan berkesan menaikkan prestasi model ini berbanding teknik-teknik sedia ada yang lain. Teknik MOMF menyediakan solusi kepada teknik bukan-dominasi bagi mengelak perangkap optima setempat. Model MOMF ini mengambil kira PAPR, BER, dan SER dalam sistem MIMO-OFDM bagi meningkatkan kecekapan sistem. Keseimbangan yang wajar antara eksplorasi-eksploitasi membantu dalam membaiki penumpuan dan mengatasi perangkap optima setempat. MOMF dalam fasa optimanisasi OFDM telah dinilai menggunakan BER, SER, dan matrik Nisbah Kuasa Puncak-kepada-Purata (PAPR). Kaedah MOMF mempunyai nilai PAPR sebanyak 3.95 dB dan kaedah PSO-GWO mempunyai PAPR 4.92 dB.
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Shruthi, G., Monica R. Mundada, B. J. Sowmya i S. Supreeth. "Mayfly Taylor Optimisation-Based Scheduling Algorithm with Deep Reinforcement Learning for Dynamic Scheduling in Fog-Cloud Computing". Applied Computational Intelligence and Soft Computing 2022 (28.08.2022): 1–17. http://dx.doi.org/10.1155/2022/2131699.
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Fog computing domain plays a prominent role in supporting time-delicate applications, which are associated with smart Internet of Things (IoT) services, like smart healthcare and smart city. However, cloud computing is a capable standard for IoT in data processing owing to the high latency restriction of the cloud, and it is incapable of satisfying needs for time-sensitive applications. The resource provisioning and allocation process in fog-cloud structure considers dynamic alternations in user necessities, and also restricted access resources in fog devices are more challenging. The global adoption of IoT-driven applications has led to the rise of fog computing structure, which permits perfect connection for mobile edge and cloud resources. The effectual scheduling of application tasks in fog environments is a challenging task because of resource heterogeneity, stochastic behaviours, network hierarchy, controlled resource abilities, and mobility elements in IoT. The deadline is the most significant challenge in the fog computing structure due to the dynamic variations in user requirement parameters. In this paper, Mayfly Taylor Optimisation Algorithm (MTOA) is developed for dynamic scheduling in the fog-cloud computing model. The developed MTOA-based Deep Q-Network (DQN) showed better performance with energy consumption, service level agreement (SLA), and computation cost of 0.0162, 0.0114, and 0.0855, respectively.
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Adnan, Rana Muhammad, Ozgur Kisi, Reham R. Mostafa, Ali Najah Ahmed i Ahmed El-Shafie. "The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction". Hydrological Sciences Journal 67, nr 2 (20.01.2022): 161–74. http://dx.doi.org/10.1080/02626667.2021.2012182.
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Fortes, Elenilson V., Luís Fabiano Barone Martins, Marcus V. S. Costa, Luis Carvalho, Leonardo H. Macedo i Rubén Romero. "Mayfly Optimization Algorithm Applied to the Design of PSS and SSSC-POD Controllers for Damping Low-Frequency Oscillations in Power Systems". International Transactions on Electrical Energy Systems 2022 (26.04.2022): 1–23. http://dx.doi.org/10.1155/2022/5612334.
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In this paper, it is proposed to apply the mayfly optimization algorithm (MOA) to perform the coordinated and simultaneous tuning of the parameters of supplementary damping controllers, i.e., power system stabilizer (PSS) and power oscillation damping (POD), that actuate together with the automatic voltage regulators of the synchronous generators and the static synchronous series compensator (SSSC), respectively, for damping low-frequency oscillations in power systems. The performance of the MOA is compared with the performances of the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm for solving this problem. The dynamics of the power system is represented using the current sensitivity model, and, because of that, a current injections model is proposed for the SSSC, which uses proportional-integral (PI) controllers and the residues of the current injections at the buses, obtained from the Newton–Raphson method. Tests were carried out using the New England system and the two-area symmetrical system. Both static and dynamic analyses of the operation of the SSSC were performed. To validate the proposed optimization techniques, two sets of tests were conducted: first, with the purpose of verifying the performance of the most effective algorithm for tuning the parameters of PSSs, PI, and POD controllers, and second, with the purpose of performing studies focused on small-signal stability. The results have validated the current injections model for the SSSC, as well as have indicated the superior performance of the MOA for solving the problem, accrediting it as a powerful tool for small-signal stability studies in power systems.
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Yan, Pengcheng, Xiaofei Zhang, Xuyue Kan, Heng Zhang, Runsheng Qi i Qingyun Huang. "Fast Identification Method of Mine Water Source Based on Laser-Induced Fluorescence Technology and Optimized LSTM". Water 15, nr 4 (10.02.2023): 701. http://dx.doi.org/10.3390/w15040701.
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There is a great threat to the production safety of coal mines caused by mine water disasters. Traditional identification methods are not adapted to the efficiency of today’s coal mining and do not offer the advantage of accurate detection in real-time. In this study, the Mayfly Algorithm (MA) was used to optimize the Long Short-Term Memory (LSTM) network, combined with laser-induced fluorescence technology, to apply it to the identification of mine water sources for the prevention of mine water disasters and post-disaster relief work. Taking sandstone water and goaf water as the original samples, five mixed water samples were also prepared by mixing the sandstone water and goaf water in different proportions, giving a total of seven water samples to be tested. Laser-induced fluorescence technology was used to obtain the fluorescence spectral data of water samples, and then the Linear Discriminant Analysis (LDA) dimensionality reduction algorithm and the Principal Component Analysis (PCA) dimensionality reduction algorithm were used to reduce the dimensions of the original spectral data. Then, three architectures, including LSTM, GA-LSTM (optimization of the LSTM by genetic algorithm) and MA-LSTM were designed to identify mine water sources. Finally, from the results’ analysis, MA-LSTM performs best in many aspects after PCA dimensionality reduction and has the best identification effect. These results supported the feasibility of the novel method.
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Mahum, Rabbia, Mohamed Sharaf, Haseeb Hassan, Lixin Liang i Bingding Huang. "A Robust Brain Tumor Detector Using BiLSTM and Mayfly Optimization and Multi-Level Thresholding". Biomedicines 11, nr 6 (15.06.2023): 1715. http://dx.doi.org/10.3390/biomedicines11061715.
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A brain tumor refers to an abnormal growth of cells in the brain that can be either benign or malignant. Oncologists typically use various methods such as blood or visual tests to detect brain tumors, but these approaches can be time-consuming, require additional human effort, and may not be effective in detecting small tumors. This work proposes an effective approach to brain tumor detection that combines segmentation and feature fusion. Segmentation is performed using the mayfly optimization algorithm with multilevel Kapur’s threshold technique to locate brain tumors in MRI scans. Key features are achieved from tumors employing Histogram of Oriented Gradients (HOG) and ResNet-V2, and a bidirectional long short-term memory (BiLSTM) network is used to classify tumors into three categories: pituitary, glioma, and meningioma. The suggested methodology is trained and tested on two datasets, Figshare and Harvard, achieving high accuracy, precision, recall, F1 score, and area under the curve (AUC). The results of a comparative analysis with existing DL and ML methods demonstrate that the proposed approach offers superior outcomes. This approach has the potential to improve brain tumor detection, particularly for small tumors, but further validation and testing are needed before clinical use.
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Biju, Roshima, Warish Patel, K. Suresh Manic i Venkatesan Rajinikanth. "Framework for Classification of Chest X-Rays into Normal/COVID-19 Using Brownian-Mayfly-Algorithm Selected Hybrid Features". Mathematical Problems in Engineering 2022 (11.08.2022): 1–13. http://dx.doi.org/10.1155/2022/6475808.
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The improvements in computation facility and technology support the development and implementation of automatic methods for medical data assessment. This study tries to extend a framework for efficiently classifying chest radiographs (X-rays) into normal/COVID-19 class. The proposed framework consists subsequent phases: (i) image resizing, (ii) deep features extraction using a pretrained deep learning method (PDLM), (iii) handcrafted feature extraction, (iv) feature optimization with Brownian Mayfly-Algorithm (BMA), (v) serial integration of optimized features, and (vi) binary classification with 10-fold cross validation. In addition, this work implements two methodologies: (i) performance evaluation of the existing PDLM in the literature and (ii) improving the COVID-19 detection performance of chosen PDLM with this proposal. The experimental investigation of this study authenticates that the effort performed using pretrained VGG16 with SoftMax helped get a classification accuracy of >94%. Further, the research performed using the proposed framework with BMA selected features (VGG16 + handcrafted features) helps achieve a classification accuracy of 99.17% on the chosen X-ray image database. This outcome proves the scientific importance of the implemented framework, and in the future, this proposal can be adopted to inspect the clinically collected X-rays.
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Kanagaraj, Sushita, i Shanmugasundaram Nithaiyan. "Speed control of hybrid energy sources fed BLDC motor drive with FOPID controllerusing various optimization techniques". Bulletin of Electrical Engineering and Informatics 11, nr 6 (1.12.2022): 3069–78. http://dx.doi.org/10.11591/eei.v11i6.4095.
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This article suggests the control of current and speed approach to reduce the torque ripple in BLDC motor. Initially, the renewable energy hybrid power system (REHPS) is composed of a generation system of PV, fuel cell (FC), and the storage system of battery bank. This REHPS uses solar power as their main source of electricity during the day. It uses the fuel cell as a secondary source for maintenance at night or during periods of shaded conditions. The novelty of the proposed method is to achieve torque ripple minimization and to control the speed of the BLDC motor. The speed and error torque of the BLDC motor is optimized by mayfly optimization algorithm (MOA). The MOA provides gain parameters of the fractional order proportional–integral–derivative (FOPID) controller. The advantage of the proposed method is to improve the level of dependability and provide flexibility in solving the system error. The proposed model is implemented in MATLAB/Simulink and experimental setup. The results of the proposed method are compared with the existing research techniques such as particle swarm optimization (PSO) and moth flame algorithm (MFA).
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Khasanov, Mansur, Salah Kamel, Furqat Nazarov, Maxzuna Rizayeva i Nozina Shodiyeva. "Optimal distributed generation allocation in distribution system for power loss minimization and voltage stability improvement". E3S Web of Conferences 401 (2023): 03071. http://dx.doi.org/10.1051/e3sconf/202340103071.
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Voltage instability and power loss are significant problems in distribution Systems (DS). However, these problems are usually mitigated by the optimal integration of distributed generation (DG) units in the DN. In this regard, the optimal location and size of the DGs are crucial. Otherwise, System performance will deteriorate. This study is conducted to place the DGs in the radial DS. Mayfly Algorithm (MA) is used to determine the optimal placement and size of the DGs to minimize power loss, increase voltage stability in radial DS. The simulation results showed a reduction in the percentage of power loss is 69.14% for three PV-type DG unit integration. The corresponding percentage of power loss reduction is 98.09 % for three WT-type DG units by installing DG units to the test System. Similarly, the minimum bus voltage stability improves to 0.959 per unit for three PV type DG unit integration. The VSI after DG allocation increases to 0.989 per unit for three WT type DG units by optimal installing DG units. Comparative studies have been conducted, and the results have shown the effectiveness of the proposed method in reducing the power loss and improving the voltage stability of the DS. The proposed algorithm is evaluated in the IEEE-69 bus radial DS using MATLAB.