Relevant bibliographies by topics / MAYFLY ALGORITHM

Contents

  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: 22 June 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'MAYFLY ALGORITHM.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "MAYFLY ALGORITHM"

1

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
Abstract:
<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>
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "MAYFLY ALGORITHM"

1

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

Full text
Abstract:
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 the very recent algorithm which is Mayfly Algorithm (MA) developed in May -2020 itself . In this project we have done a thorough review of Mayfly Algorithms and the recent developments happened in the Mayfly Algorithm and with various future applications of it.
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "MAYFLY ALGORITHM"

1

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Lizarraga, Enrique, Fevrier Valdez, Oscar Castillo, and Patricia Melin. "Fuzzy Dynamic Parameter Adaptation in the Mayfly Algorithm: Implementation of Fuzzy Adaptation and Tests on Benchmark Functions and Neural Networks." In Fuzzy Logic and Neural Networks for Hybrid Intelligent System Design. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22042-5_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "MAYFLY ALGORITHM"

1

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'MAYFLY ALGORITHM' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography