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Du, Qianhang, and Honghao Zhu. "Dynamic elite strategy mayfly algorithm." PLOS ONE 17, no. 8 (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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Zhao, Mengling, Xinlu Yang, and Xinyu Yin. "An improved mayfly algorithm and its application." AIP Advances 12, no. 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, and Le LI. "Underwater magnetic field measurement error compensation based on improved mayfly algorithm." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 40, no. 5 (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, and K. A. Gbolagade. "A proposed framework for face - iris recognition system using enhanced mayfly algorithm." Nigerian Journal of Technology 41, no. 3 (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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Zervoudakis, Konstantinos, and Stelios Tsafarakis. "A mayfly optimization algorithm." Computers & Industrial Engineering 145 (July 2020): 106559. http://dx.doi.org/10.1016/j.cie.2020.106559.
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Nagarajan, Karthik, K. Balaji Nanda Kumar Reddy, Arul Rajagopalan, NMG Kumar, and Mohit Bajaj. "Improved Mayfly Algorithm for Optimizing Power Flow with Integrated Solar and Wind Energy." International Journal of Electrical and Electronics Research 12, no. 2 (2024): 415–20. http://dx.doi.org/10.37391/ijeer.120212.
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Across the globe, the transition towards sustainable energy systems necessitates seamless implementation of Renewable Energy Sources (RES) into traditional power grids. Such RESs include solar and wind power. The current research work intends to overcome the challenges associated with Optimal Power Flow (OPF) problem in power systems in which the traditional operation parameters ought to be optimized for effective and trustworthy integration of the RESs. The current study proposes an innovative nature-inspired approach by enhancing the Mayfly algorithm on the basis of mating behaviour of mayflies. The aim of this approach is to tackle the complexities introduced by dynamic and discontinuous nature of solar and wind power. The improved Mayfly algorithm aims at minimizing power losses, emission, optimize voltage profiles, and ensure reliable integration of solar and wind power. The current study outcomes provide knowledgeable insights towards power flow optimization in power systems with high penetration of renewable energy. The application results reveal that the improved mayfly algorithm achieved better efficacy compared to the classical mayfly algorithm and the rest of the optimization algorithms.
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Seifedine, Kadry, Rajinikanth Venkatesan, Koo Jamin, and Kang Byeong-Gwon. "Image multi-level-thresholding with Mayfly optimization." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5420–29. https://doi.org/10.11591/ijece.v11i6.pp5420-5429.
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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 (BFO), firefly-algorithm (FA), bat algorithm (BA), cuckoo search (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.
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Kadry, Seifedine, Venkatesan Rajinikanth, Jamin Koo, and Byeong-Gwon Kang. "Image multi-level-thresholding with Mayfly optimization." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (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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Zhao, Juan, and Zheng-Ming Gao. "The negative mayfly optimization algorithm." Journal of Physics: Conference Series 1693 (December 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, and Yu-Rong Hu. "The improved mayfly optimization algorithm." Journal of Physics: Conference Series 1684 (November 2020): 012077. http://dx.doi.org/10.1088/1742-6596/1684/1/012077.
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Prasanna, S. L., and Nagendra Panini Challa. "Heart Disease Prediction Using Optimal Mayfly Technique with Ensemble Models." International Journal of Swarm Intelligence Research 13, no. 1 (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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Dilip Kumar Bagal, Soudamini Behera,. "Optimizing Power Generation Scheduling: A Comparative Analysis of Metaheuristic Algorithms." Journal of Electrical Systems 20, no. 2 (2024): 2212–30. http://dx.doi.org/10.52783/jes.1989.
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Purpose: This research compares six optimization methods, including the Mayfly Optimization Algorithm, Genetic Algorithm, Simulated Annealing, Firefly Algorithm, and Differential Evolution (DE).
 Design/Methodology/Approach: The evaluation of any algorithm is predicated on its ability to strike a balance between meeting demand, taking into account renewable energy sources, and lowering the total cost of producing power.
 Findings: The analysis shows that although PSO and GA converge to similar overall costs, the algorithms' performances differ. Then come SA, FA, and DE in close succession, with the Mayfly Optimization Algorithm showing higher total costs than the other techniques.
 Originality: By addressing difficult power generating scheduling problems, this work advances our understanding of the benefits and drawbacks of various optimization techniques.
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Zhao, Yanpu, Changsheng Huang, Mengjie Zhang, and Yang Cui. "AOBLMOA: A Hybrid Biomimetic Optimization Algorithm for Numerical Optimization and Engineering Design Problems." Biomimetics 8, no. 4 (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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J, Jenifer, and Jemima Priyadarsini R. "Improved Mayfly Optimization and LightGBM Classifier for Smart City Traffic Prediction." Indian Journal of Science and Technology 15, no. 40 (2022): 2085–92. https://doi.org/10.17485/IJST/v15i40.1155.
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Abstract <strong>Objectives:</strong> This research work focuses on predicting traffic for the Smart City. <strong>Methods:</strong> Current research methods for traffic prediction are based on machine learning (ML) model. This article presents two contributions related to it. First, it provides feature engineering that includes feature extraction and a nature inspired optimization algorithm for selecting the best features. The mayfly optimization algorithm is improved by using the mode-based ranking method to select the best feature. Second, it uses the light-weight boosting method to train the datasets for better accuracy.<strong>Findings:</strong> The proposed Improved MayFly Optimization with LightGBM (IMFO-LGBM) is experimented with popular smart city datasets which is available in Kaggle website. IMFOLGBM shows an improvement in the prediction accuracy when compared with the baseline methods. It shows 2% of increase in the overall accuracy. <strong>Novelty:</strong>Nature inspired Mayfly optimization is improved and used to find the best feature for prediction. The selected features are then trained with the light weight boosting algorithm (i.e., Light Gradient Boosting Model). The hybrid of improved mayfly optimization and light GBM outperformed well. <strong>Keywords:</strong> IoT; Smartcity; Mayfly optimization; Machine learning and LightGBM
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YOGA DWI WAHYU NUGRAHA, HENDRAWAN ARMANTO, and YOSI KRISTIAN. "Single Objective Mayfly Algorithm with Balancing Parameter for Multiple Traveling Salesman Problem." Journal of Electronics, Electromedical Engineering, and Medical Informatics 5, no. 3 (2023): 193–204. http://dx.doi.org/10.35882/jeemi.v5i3.299.
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The Multiple Travelling Salesman Problem (MTSP) is a challenging combinatorial problem that involves multiple salesman visiting a set of cities, each exactly once, starting and ending at the same depot. The aim is to determine the optimal route with minimal cost and node cuts for each salesman while ensuring that at least one salesman visits each city. As the problem is NP-Hard, a single-objective metaheuristic algorithm, called the Mayfly Algorithm, inspired by the collective behavior of mayflies, is employed to solve the problem using the TSPlib95 test data. Since the Mayfly Algorithm employs a single fitness function, a balancing parameter is added to perform multiobjective optimization. Three balancing parameters in the optimization process: SumRoute represents the total cost of all salesmen travelling, StdRoute balances each salesman cost, and StdNodes balances the number of nodes for each salesman. The values of these parameters are determined based on the results of various tests, as they significantly impact the MTSP optimization process. With the appropriate parameter values, the single-objective Mayfly Algorithm can produce optimal solutions and avoid premature convergence. Overall, the Mayfly Algorithm shows promise as a practical approach to solving the MTSP problem. Using multiobjective optimization with balancing parameters enables the algorithm to achieve optimal results and avoid convergence issues. The TSPlib95 dataset provides a robust testing ground for evaluating the algorithm’s effectiveness, demonstrating its ability to solve MTSP effectively with multiple salesman.
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Wang, Xing, Jeng-Shyang Pan, Qingyong Yang, Lingping Kong, Václav Snášel, and Shu-Chuan Chu. "Modified Mayfly Algorithm for UAV Path Planning." Drones 6, no. 5 (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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Chen, Zhan, Yangwang Fang, Ruitao Zhang, and Wenxing Fu. "Layout of Detection Array Based on Multi-Strategy Fusion Improved Adaptive Mayfly Algorithm in Bearing-Only Sensor Network." Sensors 24, no. 8 (2024): 2415. http://dx.doi.org/10.3390/s24082415.
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The various applications of bearing-only sensor networks for detection and localization are becoming increasingly widespread and important. The array layout of the bearing-only sensor network seriously impacts the detection performance. This paper proposes a multi-strategy fusion improved adaptive mayfly algorithm (MIAMA) in a bearing-only sensor network to perform layout planning on the geometric configuration of the optimal detection. Firstly, the system model of a bearing-only sensor network was constructed, and the observability of the system was analyzed based on the Cramer–Rao Lower Bound and Fisher Information Matrix. Then, in view of the limitations of the traditional mayfly algorithm, which has a single initial population and no adaptability and poor global search capabilities, multi-strategy fusion improvements were carried out by introducing Tent chaos mapping, the adaptive inertia weight factor, and Random Opposition-based Learning. Finally, three simulation experiments were conducted. Through comparison with the Particle Swarm Optimization (PSO) algorithm, Mayfly Algorithm (MA), and Genetic Algorithm (GA), the effectiveness and superiority of the proposed MIAMA were validated.
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Zhang, Shoujing, Tiantian Hou, Qing Qu, et al. "An Improved Mayfly Method to Solve Distributed Flexible Job Shop Scheduling Problem under Dual Resource Constraints." Sustainability 14, no. 19 (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, and Zheng-Ming Gao. "The improved mayfly optimization algorithm with Chebyshev map." Journal of Physics: Conference Series 1684 (November 2020): 012075. http://dx.doi.org/10.1088/1742-6596/1684/1/012075.
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Annisa Jamali, Aida Nur Syafiqah Shaari, Muhamad Sukri Hadi, and Intan Zaurah Mat Darus. "Mayfly Algorithm for Modelling a Horizontal Flexible Plate Structure." Journal of Advanced Research in Applied Mechanics 118, no. 1 (2024): 167–82. http://dx.doi.org/10.37934/aram.118.1.167182.
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Flexible plates are widely used in engineering and the industry, primarily due to the lightweight nature compared to rigid counterparts. These structures offer benefits such as cost savings, lower energy consumptions and improved operational safety. However, a notable drawback is that flexible structures are vulnerable to unwanted vibrations, which can cause structural damages. Hence, the development of specialized models are essential to effectively addressing this challenge. Researchers have devised various approaches to suppress unwanted vibrations, with contemporary studies often employing system identification techniques utilizing swarm intelligence algorithms to construct dynamic models of flexible structures. Therefore, this research employs the potent mayfly algorithm (MA), known for its effectiveness in optimization tasks. The developed models using MA were then compared with traditional approach known as recursive least square (RLS) through a comparative analysis. The outcome reveals that RLS exhibited the lowest mean square error (MSE) at , while MA had an MSE of Yet, MA adeptly depicted the characteristics of the system, outperforming the RLS in these validation by indicating a 95% confidence level in the correlation test and exhibiting robust stability in the pole-zero diagram. Consequently, MA serves as a fitting algorithm to accurately depict the real behaviour of the flexible plate structure.
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Abdulsalam, S. O., R. A. Ayofe, M. F. Edafeajiroke, J. F. Ajao, and R. S. Babatunde. "Development of an intrusion detection system using mayfly feature selection and artificial neural network algorithms." LAUTECH Journal of Engineering and Technology 8, no. 2 (2024): 148–60. http://dx.doi.org/10.36108/laujet/4202.81.0241.
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Protecting the privacy and confidentiality of information and devices in computer networks requires reliable methods of intrusion detection. However, effective intrusion detection is made more difficult by the enormous dimensions of data available in computer networks. To boost intrusion detection classification performance in computer networks, this study developed a feature selection mode for the classification task. The proposed model utilized the Mayfly feature selection algorithm and ANN as the classifiers. The model was also tested without a mayfly algorithm. The model's efficacy was determined through a comparison of its accuracy, specificity, precision, sensitivity, and F1 score. The experimental outcomes revealed that the proposed model is more efficient than existing models based on the performance evaluation and the CIC-IDS 2017 dataset employed in this research. Accuracy scores of 99.94% (using Data+mayfly+ANN) and 90.17% (using Data+ANN) were attained after experimentation. In comparison to existing models, the proposed model yielded better results in terms of accuracy, sensitivity, specificity, and F1-score metrics. The model's sturdiness can be attributed to the use of mayfly techniques, which harness the strength in PSO, GA and FA for selecting optimal feature subsets. The results of this research provide a reliable dimensionality reduction model that may be used in the field of computer networks for intrusion detection and enhancement of security in computer networking environments.
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Ansah, Kwabena, Justice Kwame Appati, Ebenezer Owusu, and Jamal-Deen Abdulai. "A Hybrid Heuristic Model for Duty Cycle Framework Optimization." International Journal of Distributed Sensor Networks 2024 (January 27, 2024): 1–12. http://dx.doi.org/10.1155/2024/9972429.
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This paper proposes a hybrid metaheuristic approach to optimize a duty cycle framework based on Seagull and Mayfly Optimization (HSMO-DC) Algorithm. This approach becomes crucial as current clustering protocols are unable to efficiently tune the clustering parameters in accordance to the diversification of varying WSNs. The proposed HSMO-DC primarily has two parts, where the first part takes care of the online cluster head selection and network communication using the seagull algorithm while the second part performs parameter optimization using the mayfly algorithm. The seagull is aimed at improving the energy distribution in the network through an effective bandwidth allocation procedure while reducing the total energy dissipation. Comparatively, with other clustering protocols, our proposed methods reveal an enhanced network lifetime with an improved network throughput and adaptability based on selected standard metric of performance measurement.
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Prasanth, Vidhya, M. Ramachandran, and Kurinjimalar Ramu. "A Study on Mayfly Algorithm and Its Recent Developments." Data Analytics and Artificial Intelligence 2, no. 2 (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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JESI, MARIA, AHILAN APPATHURAI, MUTHU KUMARAN, and ARUL KUMAR. "LOAD BALANCING IN CLOUD COMPUTING VIA MAYFLY OPTIMIZATION ALGORITHM." REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE 69, no. 1 (2024): 79–84. http://dx.doi.org/10.59277/rrst-ee.2024.1.14.
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Cloud computing is a new technology that enables users to store and retrieve data via the Internet on demand rather than using their hardware. Cloud computing comprises distinct data centers (servers) and clients (users). Load unbalancing is a multi-variant, multi-constraint issue that lowers the efficacy and performance of system resources. Therefore, a load scheduling technique is needed to distribute work among the right VMs and preserve the trade-off between them. To achieve better performance, this paper presents a novel mayfly optimization algorithm for load balancing (MFO-LB), which utilizes mayfly flight behavior and mating dynamics. The proposed technique balances the load in the cloud by managing the incoming loads by allocating resources according to user requests. The proposed work intends to increase performance by uniformly dividing the workload among the virtual machines, which will decrease utilization and reaction time. The proposed MFO-LB approach is beneficial for maintaining system stability, reducing response time (RT), and maximizing resource productivity in cloud environments. Finally, the effectiveness of the proposed technique is assessed by employing several metrics, including execution cost, RT, execution time, and makespan. The proposed method achieves up to 23.4 % low RT, a 24 %decrease in makespan, and a 31.5 % decrease in completion time, respectively.
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Amirthalingam, M., and R. Ponnusamy. "Enhancing Wireless Capsule Endoscopic Image Classification using Mayfly Algorithm with Deep Learning Approach." International Journal of Science and Research (IJSR) 12, no. 11 (2023): 1113–24. http://dx.doi.org/10.21275/sr231115113636.
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Li, Linfeng, Weidong Liu, Le Li, Huifeng Jiao, Junqi Qu, and Gongwu Sun. "Compensation of Optical Pump Magnetometer Using the Improved Mayfly Optimization Algorithm." Journal of Marine Science and Engineering 10, no. 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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Wang, Ji-Quan, Hong-Yu Zhang, Hao-Hao Song, Pan-Li Zhang, and Jin-Ling Bei. "Prediction of Pork Supply Based on Improved Mayfly Optimization Algorithm and BP Neural Network." Sustainability 14, no. 24 (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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Gao, Zheng-Ming, Juan Zhao, Su-Ruo Li, and Yu-Rong Hu. "The improved mayfly optimization algorithm with opposition based learning rules." Journal of Physics: Conference Series 1693 (December 2020): 012117. http://dx.doi.org/10.1088/1742-6596/1693/1/012117.
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Barhanpurkar, Atharva, Deepak Hujare, Omkar Kulkarni, and Abhijeet Birari. "Optimization of Flywheel for Reciprocating Air Compressor using Mayfly Algorithm." International Journal of Engineering Trends and Technology 71, no. 8 (2023): 191–200. http://dx.doi.org/10.14445/22315381/ijett-v71i8p217.
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Pratap Singh, Akhilendra, Manish Srivastava, K. Soumya, and Kuldeep Singh Kulhar. "Mayfly Optimization Algorithm for MPPT of PV System under Partial Shading Conditions." E3S Web of Conferences 540 (2024): 12004. http://dx.doi.org/10.1051/e3sconf/202454012004.
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Maximum power point tracking (MPPT)methods are most popular to harvest maximum energy from renewable energy sources for generating electric power. Among many renewable energy sources, solar energy is a primary and mostly available on the earth. Generally photovoltaic (PV) panel are arranging in a proper sequence to produce required electric power. Many conventional MPPT algorithms are available on PV system but working on uniform irradiances. Partial shading is a common phenomenon on PV systems. During partial shading conditions, conventional MPPT algorithms will fail to exhibits its best performance due to occurrence of many local peak powers. Hence, an efficient optimization method must be incorporated to identify global maximum power point among all possible points. Mayfly optimization is one of the best in existing optimization methods. However, perturb and observe (P&O) method must be incorporate with Mayfly technique to produce the best performance under both uniform irradiances and partial shading condition. A boost converter is used as a MPPT device in this paper due to its merits over other converters. Extensive results are carried out on OPAL-RT platform by establishing Hardware – in the – Loop (HIL) to validate the proposed method.
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Al-Karawi, Saja Bilal Hafedh, and Hakan Koyuncu. "Hybrid Neural Network Approach for Tea Leaf Disease Detection Using Pelican and Mayfly Optimization Algorithms." Jurnal Riset Informatika 6, no. 2 (2024): 119–30. http://dx.doi.org/10.34288/jri.v6i2.274.
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This study addresses the problem of plant diseases and the difficulty of detecting them, and it presents a unique technique for the automatic detection of tea leaf diseases by combining neural networks and optimization techniques. Our research uses a curated database of tea plant leaf photographs that includes healthy and diseased specimens. The neural network (CNN) is trained and fine-tuned using optimization algorithms. To increase disease identification accuracy, we used a hybrid novel optimization algorithm called (POA-MA) which is Pelican Optimization Algorithm (POA), and Mayfly Optimization Algorithm (MA) for feature selection, followed by classification with Support Vector Machine (SVM). The suggested mechanism performance is evaluated using accuracy, MSE, F-score, recall, and sensitivity measures. The suggested CNN-POAMA hybrid model yielded 94.5%, 0.035, 0.91, 0.93, and 0.92, respectively. This study advances precision agriculture by establishing a strong framework for automated detection, allowing for early intervention, and eventually enhancing tea crop health.
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Liu, Ximu, Mi Zhao, Zihan Wei, and Min Lu. "Economic Optimal Scheduling of Wind–Photovoltaic-Storage with Electric Vehicle Microgrid Based on Quantum Mayfly Algorithm." Applied Sciences 12, no. 17 (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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Hu, Zhixiang, Huiyu Zhu, Lei Huang, and Cheng Cheng. "Damage Identification Method and Uncertainty Analysis of Beam Structures Based on SVM and Swarm Intelligence Algorithm." Buildings 12, no. 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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Yousaf, Muhammad Zain, Ali Raza, Ghulam Abbas, et al. "MTDC Grids: A Metaheuristic Solution for Nonlinear Control." Energies 15, no. 12 (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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Kyomugisha, Rebeccah, Christopher Maina Muriithi, and 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 (April 29, 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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Bhattacharyya, Trinav, Bitanu Chatterjee, Pawan Kumar Singh, Jin Hee Yoon, Zong Woo Geem, and 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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Dodda, Ratnam, and Alladi Suresh Babu. "Text document clustering using mayfly optimization algorithm with k-means technique." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 2 (2024): 1099. http://dx.doi.org/10.11591/ijeecs.v35.i2.pp1099-1109.
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Text clustering is a subfield of machine learning (ML) and natural language processing (NLP) that consists of grouping similar sentences or documents based on their content. However, insignificant features in the documents minimize the accuracy of information retrieval which makes it challenging for the clustering approach to efficiently cluster similar documents. In this research, the mayfly optimization algorithm (MOA) with a k-means approach is proposed for text document clustering (TDC) to effectively cluster similar documents. Initially, the data is obtained from Reuters-21678, 20-Newsgroup, and BBC sports datasets, and then pre-processing is established by stemming and stop word removal to remove unwanted phrases or words. The data imbalance approach is established using an adaptive synthetic sampling algorithm (ADASYN), then term frequency-inverse document frequency (TD-IDF) and WordNet features are employed for extracting features. Finally, MOA with the K-means technique is utilized for TDC. The proposed approach achieves better accuracy of 99.75%, 99.54%, and 98.24% when compared to the existing techniques like fuzzy rough set-based robust nearest neighbor-convolutional neural network (FRS-RNN-CNN), TopicStriker, Modsup-based frequent itemset, and rider optimization-based moth search algorithm (Modsup-Rn-MSA), hierarchical dirichlet-multinomial mixture, and multi-view clustering via consistent and specific non-negative matrix (MCCS).
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Ratnam, Dodda Alladi Suresh Babu. "Text document clustering using mayfly optimization algorithm with k-means technique." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 2 (2024): 1099–109. https://doi.org/10.11591/ijeecs.v35.i2.pp1099-1109.
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Text clustering is a subfield of machine learning (ML) and natural language processing (NLP) that consists of grouping similar sentences or documents based on their content. However, insignificant features in the documents minimize the accuracy of information retrieval which makes it challenging for the clustering approach to efficiently cluster similar documents. In this research, the mayfly optimization algorithm (MOA) with a k-means approach is proposed for text document clustering (TDC) to effectively cluster similar documents. Initially, the data is obtained from Reuters-21678, 20-Newsgroup, and BBC sports datasets, and then pre-processing is established by stemming and stop word removal to remove unwanted phrases or words. The data imbalance approach is established using an adaptive synthetic sampling algorithm (ADASYN), then term frequency-inverse document frequency (TD-IDF) and WordNet features are employed for extracting features. Finally, MOA with the K-means technique is utilized for TDC. The proposed approach achieves better accuracy of 99.75%, 99.54%, and 98.24% when compared to the existing techniques like fuzzy rough set-based robust nearest neighbor-convolutional neural network (FRS-RNN-CNN), TopicStriker, Modsup-based frequent itemset, and rider optimization-based moth search algorithm (Modsup-Rn-MSA), hierarchical dirichlet-multinomial mixture, and multi-view clustering via consistent and specific non-negative matrix (MCCS).
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Mukase, Sandrine, and Kewen Xia. "Multi-Objective Optimization with Mayfly Algorithm for Periodic Charging in Wireless Rechargeable Sensor Networks." World Electric Vehicle Journal 13, no. 7 (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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Olaniyan, Olatayo Moses, Ayobami Taiwo Olusesi, Bolaji Abigail Omodunbi, Wajeed Bolanle Wahab, Olusogo Julius Adetunji, and 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, no. 3 (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, and 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, no. 3 (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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Hu, Aihua, Zhongliang Deng, Hui Yang, Yao Zhang, Yuhui Gao, and Di Zhao. "An Optimal Geometry Configuration Algorithm of Hybrid Semi-Passive Location System Based on Mayfly Optimization Algorithm." Sensors 21, no. 22 (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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Qassem, Deema Yahya, and Najla Akram Al_saati. "A Solution to the Next Release Problem by Swarm Intelligence." Technium: Romanian Journal of Applied Sciences and Technology 12 (August 22, 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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Mo, Shixun, Qintao Ye, Kunping Jiang, Xiaofeng Mo, and Gengyu Shen. "An improved MPPT method for photovoltaic systems based on mayfly optimization algorithm." Energy Reports 8 (August 2022): 141–50. http://dx.doi.org/10.1016/j.egyr.2022.02.160.
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Jayagayathri, I., and C. Mythili. "Inertia weight updated mayfly optimisation algorithm-based thermal breast cancer image segmentation." International Journal of Bio-Inspired Computation 22, no. 3 (2023): 139–51. http://dx.doi.org/10.1504/ijbic.2023.135469.
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Jaber, Fatimah. "PID CONTROLLER FOR SPEED CONTROL OF PMSM BASED ON MAYFLY OPTIMIZATION ALGORITHM." Kufa Journal of Engineering 16, no. 1 (2025): 104–20. https://doi.org/10.30572/2018/kje/160107.
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Permanent magnet synchronous motor (PMSM) is extensively employed in AC servo drives owing to their superior torque-to-inertia ratio, power density, efficiency, and power factor compared to other motors. So, it is a crucial point to regulate the PMSM speed. Conventional proportional, integral, and differential (PID) is a simple controller and easy to implement but it is coefficients are essentially determined by experience when used in PMSM to control the speed. This invariably produces unacceptable outcomes, in addition when it comes to low-power application drives, PID controller gains typically produce adequate results but, when it comes to high-power application drives, an untuned PID does not deliver satisfactory performance. The optimization algorithm offers an effective method to produce optimal PID gains. Therefore, to optimize the PID coefficients to regulate the PMSM speed, this study suggests a mayfly optimization algorithm (MA). Recently, the MA was introduced as a new intelligent optimization method with exceptional optimization capabilities. Nuptial dancing and random flight improve the ability of the algorithm to balance its features of exploitation and exploration while assisting in its escape from local optima. This suggested approach has been verified with MATLAB, and the outcomes are compared with the standard particle swarm optimization technique (PSO) and conventional PID. The outcomes show that compared to the standard PSO or conventional PID, the PID parameters adjusted by the MA method can produce faster speed responses and less overshoot. Furthermore, the system's optimal ITAE index value, as determined by the MA technique, is smaller (0.794) as compared to other techniques 1.503 and 1.906 respectively.
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Boopathi, Dhanasekaran, Kaliannan Jagatheesan, Baskaran Anand, Sourav Samanta, and Nilanjan Dey. "Frequency Regulation of Interlinked Microgrid System Using Mayfly Algorithm-Based PID Controller." Sustainability 15, no. 11 (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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Qian, Weifeng, Hao Sun, Peng Shi, and Imre Rudas. "A General Chip Subpixel Segmentation Localization Method Based on Improved Mayfly Algorithm." Acta Polytechnica Hungarica 21, no. 10 (2024): 331–48. http://dx.doi.org/10.12700/aph.21.10.2024.10.21.
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Yildiz, Betül Sultan, Sujin Bureerat, Natee Panagant, Pranav Mehta, and Ali Riza Yildiz. "Reptile search algorithm and kriging surrogate model for structural design optimization with natural frequency constraints." Materials Testing 64, no. 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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Lohat, Savita, Sheilza Jain, and 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.