Relevant bibliographies by topics / MAYFLY ALGORITHM

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  2. Dissertations / Theses
  3. Book chapters
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Academic literature on the topic 'MAYFLY ALGORITHM'

Author: Grafiati
Published: 11 September 2023

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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 (August 25, 2022): e0273155. http://dx.doi.org/10.1371/journal.pone.0273155.

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The mayfly algorithm (MA), as a newly proposed intelligent optimization algorithm, is found that easy to fall into the local optimum and slow convergence speed. To address this, an improved mayfly algorithm based on dynamic elite strategy (DESMA) is proposed in this paper. Specifically, it first determines the specific space near the best mayfly in the current population, and dynamically sets the search radius. Then generating a certain number of elite mayflies within this range. Finally, the best one among the newly generated elite mayflies is selected to replace the best mayfly in the current population when the fitness value of elite mayfly is better than that of the best mayfly. Experimental results on 28 standard benchmark test functions from CEC2013 show that our proposed algorithm outperforms its peers in terms of accuracy speed and stability.
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Zervoudakis, Konstantinos, 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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Zhao, Mengling, Xinlu Yang, and Xinyu Yin. "An improved mayfly algorithm and its application." AIP Advances 12, no. 10 (October 1, 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 (October 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 (November 2, 2022): 535–41. http://dx.doi.org/10.4314/njt.v41i3.13.

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Fused biometrics systems have proven to solve some problems associated with unimodal systems but also face challenges in various aspects of their implementation such as difficulty in design, user acceptance is quite low, and the performance tradeoff. This framework tends to address some of these implementation challenges by using an enhanced mayfly algorithm, a modification of the existing mayfly algorithm that was recently proposed, as feature selection. Mayfly algorithm combines advantages of particle swarm optimization, genetic algorithm, and firefly algorithm, simulated in different experiments using varied benchmark function on conventional mayfly algorithm all tested to be capable of optimization, but despite its capabilities, some limitations such as slow convergent or premature convergent rate and possible imbalance between exploration and exploitation still remain unresolved, which necessitated enhancement for better performance. This framework will enhance the existing mayfly algorithm by expanding the search space which limited the ability of the conventional mayfly algorithm to be used to solve high-dimensional problem spaces such as feature selection and modify the selection procedure to model the attraction process as a deterministic process, that will be used for the feature selection procedure on fused face –iris recognition system. This will increase the capabilities of the mayfly algorithm and in turn, increase the recognition accuracy, and reduced the false acceptance rate, false rejection rate, and time complexity of the fused face–iris recognition system.
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Zhao, Juan, 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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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 (December 1, 2021): 5420. http://dx.doi.org/10.11591/ijece.v11i6.pp5420-5429.

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<span>Image thresholding is a well approved pre-processing methodology and enhancing the image information based on a chosen threshold is always preferred. This research implements the mayfly optimization algorithm (MOA) based image multi-level-thresholding on a class of benchmark images of dimension 512x512x1. The MOA is a novel methodology with the algorithm phases, such as; i) Initialization, ii) Exploration with male-mayfly (MM), iii) Exploration with female-mayfly (FM), iv) Offspring generation and, v) Termination. This algorithm implements a strict two-step search procedure, in which every Mayfly is forced to attain the global best solution. The proposed research considers the threshold value from 2 to 5 and the superiority of the result is confirmed by computing the essential Image quality measures (IQM). The performance of MOA is also compared and validated against the other procedures, such as particle-swarm-optimization (PSO), bacterial foraging optimization</span><span>(BFO), </span><span lang="EN-IN">firefly-algorithm</span><span>(FA), bat algorithm (BA), cuckoo search</span><span>(CS) and moth-flame optimization (MFO) and the attained p-value of Wilcoxon rank test confirmed the superiority of the MOA compared with other algorithms considered in this work</span>
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Prasanna, S. L., and Nagendra Panini Challa. "Heart Disease Prediction Using Optimal Mayfly Technique with Ensemble Models." International Journal of Swarm Intelligence Research 13, no. 1 (January 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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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 (August 21, 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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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 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.
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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, 347–57. Singapore: 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, 580–90. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6372-7_63.

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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, 169–83. Singapore: 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, 119–35. Cham: 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, 243–55. Singapore: 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, 57–66. Cham: 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, 53–64. Singapore: 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, 223–39. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08266-5_15.

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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, 69–84. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22042-5_4.

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Kesavan, Sujatha, Sivanand R., Rengammal Sankari B., Latha B., Tamilselvi C., and Krishnaveni S. "Deep Learning Neural Networks for Online Monitoring of the Combustion Process From Flame Colour in Thermal Power Plants." In Convergence of Deep Learning and Internet of Things, 224–44. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-6275-1.ch011.

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The combustion quality determination in power station boilers is of great importance to avoid air pollution. Complete combustion minimizes the exit of NOx, SOx, CO, and CO2 emissions, also ensuring the consistency in load generation in thermal power plants. This chapter proposes a novel hybrid algorithm, called black widow optimization algorithm with mayfly optimization algorithm (BWO-MA), for solving global optimization problems. In this chapter, an effort is made to develop BWO-MA with artificial neural networks (ANN)-based diagnostic model for onset detection of incomplete combustion. Comparison has been done with existing machine learning methods with the proposed BWO-MA-based ANN architecture to accommodate the greater performance. The comprehensive analysis showed that the proposed achieved splendid state-of-the-art performance.
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Conference papers on the topic "MAYFLY ALGORITHM"

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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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ZHAO, Juan, and Zheng-Ming GAO. "Bare bones 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/mlbdbi51377.2020.00051.

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GAO, Zheng-Ming, Su-Ruo LI, Juan ZHAO, and Yu-Rong HU. "Self-organizing hierarchical mayfly optimization algorithm." In 2020 International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). IEEE, 2020. http://dx.doi.org/10.1109/icbase51474.2020.00081.

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ZHAO, Juan, and Zheng-Ming GAO. "The fully informed mayfly optimization algorithm." In 2020 International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). IEEE, 2020. http://dx.doi.org/10.1109/icbase51474.2020.00101.

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Zhao, Juan, and Zheng-Ming Gao. "The multi-start mayfly optimization algorithm." In 2020 7th International Forum on Electrical Engineering and Automation (IFEEA). IEEE, 2020. http://dx.doi.org/10.1109/ifeea51475.2020.00184.

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Gao, Zheng-Ming, Su-Ruo Li, Juan Zhao, and Yu-Rong Hu. "The guaranteed convergence mayfly optimization algorithm." In 2020 7th International Forum on Electrical Engineering and Automation (IFEEA). IEEE, 2020. http://dx.doi.org/10.1109/ifeea51475.2020.00179.

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Zhang, Jia-Hao, Zheng-Ming Gao, Su-Ruo Li, and Juan Zhao. "Improved Mayfly Optimization Algorithm with Cooperation." In 2022 7th International Conference on Computer and Communication Systems (ICCCS). IEEE, 2022. http://dx.doi.org/10.1109/icccs55155.2022.9846576.

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He, Shaojie, Bihui Yu, Jingxuan Wei, and Liping Bu. "MMES: Improved Mayfly Algorithm Based on Electrostatic Optimization Algorithm." In 2022 IEEE 8th International Conference on Computer and Communications (ICCC). IEEE, 2022. http://dx.doi.org/10.1109/iccc56324.2022.10065995.

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