Relevant bibliographies by topics / MAYFLY ALGORITHM

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'MAYFLY ALGORITHM'

Author: Grafiati
Published: 11 September 2023
Last updated: 31 July 2025

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Journal articles on the topic "MAYFLY ALGORITHM"

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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 curren
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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
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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 fa
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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 experi
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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 mayfl
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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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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 May
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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 whi
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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
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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 succe
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Dissertations / Theses on the topic "MAYFLY ALGORITHM"

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JAIN, AKASH. "SYSTEMATIC STUDY OF MAYFLY ALGORITHM WITH APPLICATIONS." Thesis, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18985.

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In Anthropology there is theory of Evolution by Charles Darwin based on the concept of Survival of the fittest. So as a consequence of it every living organism be it human beings , animals , insects, or even micro-organisms like Coronavirus have to adapt , mitigate and become resilient with environment if they want to survive . That means there is a constant learning with some feedback error so that the species will introduce desired changes in them. That particular thing (Learning with feedback) is the backbone of Soft Computing. In light of Bio-Inspired Computing we are dealing with
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Book chapters on the topic "MAYFLY ALGORITHM"

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Jain, Akash, and Anjana Gupta. "Review on Recent Developments in the Mayfly Algorithm." In Algorithms for Intelligent Systems. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5747-4_30.

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Shi, Lijuan, Zhou Feng, Yiyu Sang, Xinlin Xie, and Xinying Xu. "Neighborhood Rough Set Reduction with Improved Mayfly Optimization Algorithm." In Proceedings of 2021 Chinese Intelligent Automation Conference. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6372-7_63.

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Lizárraga, Enrique, Fevrier Valdez, Oscar Castillo, and Patricia Melin. "Mayfly Algorithm with Automatic Parameter Adaptation with Fuzzy Logic." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-67195-1_49.

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Thakur, Gauri, and Ashok Pal. "Performance Analysis of Mayfly Algorithm for Problem Solving in Optimization." In Proceedings on International Conference on Data Analytics and Computing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3432-4_14.

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Singh Verma, Abhishek, Ankur Choudhary, Shailesh Tiwari, and Bhuvan Unhelkar. "An Efficient Regression Test Cases Selection & Optimization Using Mayfly Optimization Algorithm." In Springer Series in Reliability Engineering. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05347-4_8.

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Singh, Anitesh Kumar, Kalinga Simant Bal, Dipanjan Dey, Abhishek Rudra Pal, Dilip Kumar Pratihar, and Asimava Roy Choudhury. "Optimization of Wire-EDM Process Parameters for Ti6Al4V Alloy Cutting Using Mayfly Algorithm." In Lecture Notes in Mechanical Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-7150-1_20.

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Kadry, Seifedine, Venkatesan Rajinikanth, Gautam Srivastava, and Maytham N. Meqdad. "Mayfly-Algorithm Selected Features for Classification of Breast Histology Images into Benign/Malignant Class." In Mining Intelligence and Knowledge Exploration. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21517-9_6.

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Muthukumar, T., K. Jagatheesan, and Sourav Samanta. "Mayfly Algorithm-Based PID Controller for LFC of Multi-sources Single Area Power System." In Intelligence Enabled Research. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0489-9_5.

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Lizarraga, Enrique, Fevrier Valdez, Oscar Castillo, and Patricia Melin. "Fuzzy Dynamic Parameter Adaptation in the Mayfly Algorithm: Preliminary Tests for a Parameter Variation Study." In Studies in Computational Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08266-5_15.

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Zhao, Yanpu, Faming Gong, Yuhao Zhou, et al. "A Discrete Mayfly Optimization Algorithm for the Traveling Salesman Problem and Its Application in Automated Guided Vehicle Routing Optimization." In Communications in Computer and Information Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-9955-1_17.

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Conference papers on the topic "MAYFLY ALGORITHM"

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Al-Jawahry, Hassan M., E. S. Challaraj Emmanuel, Boddu Rajasekhar, R. Padmavathy, and N. Sasirekha. "Gastrointestinal Disease Classification using Mayfly Optimization Algorithm based Deep Belief Network." In 2024 First International Conference on Software, Systems and Information Technology (SSITCON). IEEE, 2024. https://doi.org/10.1109/ssitcon62437.2024.10797008.

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Pang, Tao, Chenghao Li, Hong Xu, Fei Xia, Mingke Gao, and Shiyu Gan. "A Multi-UAV Path Planning Method for Simultaneous Arrival Based on an Improved Mayfly Algorithm." In 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE). IEEE, 2025. https://doi.org/10.1109/icaace65325.2025.11019632.

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Phuangchaosuan, Suwatchari, Accarat Chaoumead, Duanraem Phaengkieo, et al. "Optimization of Magnetically Coupled Resonant Low Frequency Wireless Power Transfer Based on Mayfly Optimization Algorithm." In 2024 International Conference on Materials and Energy: Energy in Electrical Engineering (ICOME-EE). IEEE, 2024. https://doi.org/10.1109/icome-ee64119.2024.10845411.

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Hao, Zelin. "Enhancing Machine Learning for Employee Satisfaction Prediction Using MA-SOM: A Mayfly Algorithm Optimized Approach." In 2024 IEEE 2nd International Conference on Sensors, Electronics and Computer Engineering (ICSECE). IEEE, 2024. http://dx.doi.org/10.1109/icsece61636.2024.10729346.

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Baskaran, J., P. I. D. T. Bala Durai Kannan, and K. Ravi. "Optimal Size & Allocation of Distribution Generation / Distribution Static Compensator using Modified Mayfly Optimization Algorithm." In 2025 International Conference on Computational Innovations and Engineering Sustainability (ICCIES). IEEE, 2025. https://doi.org/10.1109/iccies63851.2025.11032363.

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Wu, Song, Bin Xu, Yalong Yang, and Tao Chen. "Optimizing LSTM for medium and long-term electricity load forecasting based on the improved mayfly algorithm." In 2024 6th International Conference on Energy Systems and Electrical Power (ICESEP). IEEE, 2024. http://dx.doi.org/10.1109/icesep62218.2024.10652084.

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Prasad, Lal Bahadur, and Rajan Kumar. "An Optimal Load Frequency Regulation Scheme for Isolated Multi-Source Hybrid Power System with Renewables Using Mayfly Optimization Algorithm." In 2024 IEEE Third International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES). IEEE, 2024. http://dx.doi.org/10.1109/icpeices62430.2024.10719288.

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GAO, Zheng-Ming, Su-Ruo LI, Juan ZHAO, and Yu-Rong HU. "Heterogeneous mayfly optimization algorithm." In 2020 2nd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI). IEEE, 2020. http://dx.doi.org/10.1109/mlbdbi51377.2020.00049.

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Zhao, Juan, and Zheng-Ming Gao. "The regrouping mayfly optimization algorithm." In 2020 7th International Forum on Electrical Engineering and Automation (IFEEA). IEEE, 2020. http://dx.doi.org/10.1109/ifeea51475.2020.00214.

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Gao, Zheng-Ming, Su-Ruo Li, Juan Zhao, and Yu-Rong Hu. "The constricted mayfly optimization algorithm." In 2020 7th International Forum on Electrical Engineering and Automation (IFEEA). IEEE, 2020. http://dx.doi.org/10.1109/ifeea51475.2020.00205.

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