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mayfly algorithm with elementary functions and mathematical spirals for task scheduling in cloud computing system

JOURNAL OF SUPERCOMPUTING 2025年第6期81卷 1-46页

作者： Sui, Xiao-Fei Zhang, Si-Wen Wang, Jie-Sheng Zhang, Shi-Hui Zhang, Yun-Hao Bai, Xue-Lian Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan Liaoning Peoples R China

As an Internet-based computing model, cloud computing realizes the elastic scaling and efficient utilization of resources by centralizing computing resources (such as servers, storage and networks) to form resource pools and provide services to users on-demand. Task scheduling directly affects the operational efficiency, load balancing and energy consumption of the entire system. In order to improve the execution efficiency of task scheduling in cloud computing system, a Cycloid spiral X mayfly algorithm (CXMA) based on improved dance damping ratio and dance mode was proposed. Firstly, the elementary function is used to improve the dance damping ratio, which effectively improves the convergence stability of the algorithm and better balances the ability of global exploration and local development, so that the algorithm can locate the optimal solution more accurately while maintaining high search diversity. On the basis of improving the dance damping ratio, the basic mathematical function is used to improve the dance mode of the mayfly, and the search efficiency and solution accuracy of MA are significantly improved by optimizing the search behavior of the individual mayfly so as to improve the robustness and adaptability of MA. Through simulation experiments, the total cost, time cost, load cost and price cost of the system under large-scale and small-scale tasks are tested. Comparing the proposed CXMA with other swarm intelligence optimization algorithms, the experimental results show that the proposed CXMA has significant advantages in searching for the optimal task scheduling strategy. In terms of total cost, CXMA is 6.7% lower than ACO, 0.7% lower than CDO, 3.7% lower than WOA, 4.0% lower than BOA, 2.6% lower than AOA, 1.6% lower than SOA and 3.0% lower than RSO.

关键词： Cloud computing Task scheduling Metaheuristics mayfly algorithm Elementary function Mathematical spiral

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Multi-strategy fusion mayfly algorithm on task offloading and scheduling for IoT-based fog computing multi-tasks learning

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ARTIFICIAL INTELLIGENCE REVIEW 2025年第5期58卷 1-46页

作者： Sui, Xiao-Fei Wang, Jie-Sheng Zhang, Shi-Hui Zhang, Si-Wen Zhang, Yun-Hao Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan Liaoning Peoples R China

The rapid development of Internet of Things (IoT) technology has accumulated a large amount of data, which needs to be stored, processed and deeply analyzed to meet the specific goals and needs of users. As an emerging computing model, Fog computing can allocate a large number of computing resources reasonably. In order to solve the problem of insufficient population diversity and low algorithm efficiency, Aiming at the task scheduling problem of Bag-of-Tasks(BoT) application in cloud and fog environment, a multi-strategy fusion mayfly algorithm was proposed. The method of improving the individual learning coefficient and the global learning coefficient is used to significantly improve the convergence speed, local search ability, and global search ability, and then the method of improving the social positive attraction coefficient is used to balance the development and exploration ability of the algorithm and help the algorithm to get rid of the local optimum. The main goal of the logarithm mayfly algorithm (lMA) is to complete the tasks of the IoT task package in the fog system efficiently with low cost in terms of reducing execution time and operating costs. The improved algorithms were compared with mayfly algorithm (MA), Genetic algorithm (GA), Grey Wolf Optimizer (GWO), Tyrannosaurus Optimization algorithm (TROA), Harris Hawks Optimization (HHO), Reptile Search algorithm (RSA) and Red-Tailed Hawk algorithm (RTH), and the results showed that lMA was significantly improved in terms of reducing processing time and operating cost. The performance of lMA is verified, and the results show that the model can not only save transmission energy consumption but also have good convergence.

关键词： Internet of things Fog computing Scheduling optimization Task offloading mayfly algorithm Multitasking

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mayfly algorithm with Automatic Parameter Adaptation with Fuzzy Logic

Mayfly Algorithm with Automatic Parameter Adaptation with Fu...

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International Conference on Intelligent and Fuzzy Systems (INFUS)

作者： Lizarraga, Enrique Valdez, Fevrier Castillo, Oscar Melin, Patricia Tijuana Inst Technol TecNM Tomas Aquino Tijuana Mexico

ISBN: (纸本)9783031671944;9783031671951

Inspired by the behavior of ephemeral insects, the mayfly algorithm was developed, considering their short lifespan and mating patterns for continuous evolution. It represents a significant improvement over the particle swarm optimization algorithm, combining the intelligence observed in these insects with evolutionary algorithms. A modification to its parameters using type-1 fuzzy logic was proposed to improve convergence efficiency. mayfly exhibits good exploitation but poor exploration;however, hybridizationwith fuzzy parameter adaptation, using parameters a1 and mu, enhances its performance. This deviation from local optima improves the algorithm's diversity, favoring exploration. Out of the 10 chosen benchmark functions, mayfly outperforms the original in 6 of them. Future work includes optimizing the membership functions of the fuzzy adapter using a genetic algorithm and incorporating an adapter with type-2 fuzzy logic. Such adaptations could further enhance the algorithm's performance and exploration capabilities.

关键词： mayfly algorithm Fuzzy logic

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A Novel mayfly algorithm with Response Surface for Static Damage Identification Based on Multiple Indicators

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INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS 2024年第17期24卷 2550071-2550071页

作者： Wu, Zhifeng Song, Yanpeng Chen, Hui Huang, Bin Fan, Jian Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China Wuhan Inst Technol Coll Post & Telecommun Wuhan 430073 Peoples R China Wuhan Inst Technol Sch Civil Engn & Architecture Wuhan 430073 Peoples R China Wuhan Univ Technol Sch Civil Engn & Architecture Wuhan 430070 Peoples R China Wuhan Univ Technol Hainan Inst Sanya 572000 Peoples R China

This paper proposes a novel structural damage identification approach coupling the mayfly algorithm (MA) with static displacement-based response surface (RS). Firstly, a hybrid optimal objective function is established that simultaneously considers the sensitivity-based residual errors of static damage identification equation and the static displacement residual. In the objective function, the static damage identification equation is addressed by the Tikhonov regularization technique. The MA is subsequently employed to conduct an optimal search and pinpoint the location and intensity of damages at the structural element level. To handle the inconformity of the static loading points and the measurement points of displacements, the model reduction and displacement extension techniques are implemented to reconstruct the static damage identification equation. Meanwhile, the static displacement-based RS is constructed to calculate the displacement residual in the hybrid objective function, thereby circumventing the time-consuming finite element calculations and improving computational efficiency. The identification results of the numerical box girder bridge demonstrate that the proposed method outperforms the particle swarm optimization, differential evolution, Jaya and whale optimization algorithms about both convergence rate in optimal searching and identification accuracy. The proposed method enables more accurate damage identification compared to methods solely based on the indicator of the residual of static damage identification equations or displacement residual. The results of identifying damage in the 21 element-truss structure and the static experiments on identifying damage in an aluminum alloy cantilever beam confirm the high efficiency of the proposed approach.

关键词： Static damage identification mayfly algorithm response surface static residual errors model reduction displacement extension

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An Improved mayfly algorithm With Shading Detection for MPPT of Photovoltaic Systems

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IEEE ACCESS 2023年 11卷 110827-110836页

作者： Xu, Hengshan Zhao, Mingyang Xue, Fei Zhang, Xujun Sun, Leijie China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443002 Peoples R China State Grid Ningxia Elect Power Co Ltd Elect Power Res Inst Yinchuan 750001 Peoples R China State Grid Gansu Elect Power Res Inst Lanzhou 730070 Peoples R China XJ Grp Corp Xuchang 461001 Peoples R China

A maximum power point tracking (MPPT) method based on improved mayfly algorithm (IMA) with shading detection is proposed to realize the global MPPT with multiple-peak P-V characteristic curve under partial shading condition (PSC) and rapid MPPT with single-peak P-V characteristic curve under uniform irradiance condition (UIC) for photovoltaic array in this paper. Firstly, the characteristic of current-voltage curve in the ideal current source region for photovoltaic array is analyzed, and a shading detection strategy is proposed to monitor the shading condition of photovoltaic array to identify the multi-peak and single-peak on P-V curve. Secondly, the IMA with the elimination strategy is proposed to realize multi-peak MPPT. Meanwhile, the IMA with the bisection searching strategy is utilized to realize quick single-peak MPPT. Finally, simulation and experiment are conducted to verify the effectiveness of the proposed IMA MPPT method, and the results show that the IMA MPPT method can not only identify multi-peak and single-peak effectively, but also improve the tracking efficiency and accuracy in single-peak and multi-peak MPPT scenarios.

关键词： Maximum power point tracking shading detection mayfly algorithm partial shading condition

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Pattern Synthesis of Uniform and Sparse Linear Antenna Array Using mayfly algorithm

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IEEE ACCESS 2021年 9卷 77954-77975页

作者： Owoola, Eunice Oluwabunmi Xia, Kewen Wang, Ting Umar, Abubakar Akindele, Romoke Grace Hebei Univ Technol Sch Elect & Informat Engn Tianjin 300401 Peoples R China Peoples Liberat Army Air Force 93756 Tianjin 300000 Peoples R China Hebei Univ Technol Sch Mech Engn Tianjin 300401 Peoples R China

Pattern synthesis is a significant research focus in smart antennas due to its extensive use in several radar and communication systems. To improve the optimization performance of pattern synthesis of uniform and sparse linear antenna array, this paper presents an optimization method for solving the antenna array synthesis problem using the mayfly algorithm (MA). MA is a new heuristic algorithm inspired by the flight behavior as well as the mating process of mayflies, it has a unique velocity update system with great convergence. In this work, the MA was applied to linear antenna arrays (LAA) for optimal pattern synthesis in the following ways: by optimizing the antenna current amplitudes while maintaining uniform spacing and by optimizing the antenna positions while assuming a uniform excitation. Constraints of inter-element spacing and aperture length are imposed in the synthesis of sparse LAA. Sidelobe level (SLL) suppression with the placement of nulls in the specified directions is also implemented. The results gotten from this novel algorithm are validated by benchmarking with results obtained using other intelligent algorithms. In the synthesis of uniform 20-element LAA with nulls, MA achieved an SLL of -31.27 dB and the deepest null of -101.60 dB. Also, for sparse 20-element LAA, an SLL of -18.85 dB was attained alongside the deepest null of -87.37 dB. MA obtained an SLL of -35.73 dB and -23.68 dB for the synthesis of uniform and sparse 32-element LAA respectively. Finally, electromagnetism simulations are conducted using ANSYS Electromagnetics (HFSS) software, to evaluate the performance of MA for the beam pattern optimizations, taking into consideration the mutual coupling effects. The results prove that optimization of LAA using MA provides considerable enhancements in peak SLL suppression, null control, and convergence rate with respect to the uniform array and the synthesis obtained from other existing optimization techniques.

关键词： Optimization Linear antenna arrays Genetic algorithms Convergence Adaptive arrays Particle swarm optimization Antenna radiation patterns Linear antenna array pattern synthesis mayfly algorithm

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An improved mayfly algorithm based on Kapur entropy for multilevel thresholding color image segmentation

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第1期44卷 365-380页

作者： Zhao, Xiaohan Zhu, Liangkuan Wu, Bowen Northeast Forestry Univ Coll Mech & Elect Engn Harbin Heilongjiang Peoples R China Suihua Univ Sch Elect Engn Suihua Heilongjiang Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China

Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, an improved mayfly algorithm (IMA)-based color image segmentation method is proposed. Tent mapping initializes the female mayfly population to increase population diversity. Levy flight is introduced in the wedding dance iterative formulation to make IMA jump from the local optimal solution quickly. Two nonlinear coefficients were designed to speed up the convergence of the algorithm. To better verify the effectiveness, eight benchmark functions are used to test the performance of IMA. The average fitness value, standard deviation, and Wilcoxon rank sum test are used as evaluation metrics. The results show that IMA outperforms the comparison algorithm in terms of search accuracy. Furthermore, Kapur entropy is used as the fitness function of IMA to determine the segmentation threshold. 10 Berkeley images are segmented. The best fitness value, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and other indexes are used to evaluate the effect of segmented images. The results show that the IMA segmentation method improves the segmentation accuracy of color images and obtains higher quality segmented images.

关键词： Non-linear attraction coefficients Tent chaotic mapping Levy flight color image segmentation mayfly algorithm

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Prediction of cold chain logistics temperature using a novel hybrid model based on the mayfly algorithm and extreme learning machine

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INDUSTRIAL MANAGEMENT & DATA SYSTEMS 2022年第3期122卷 819-840页

作者： Lim, Ming K. Li, Yan Wang, Chao Tseng, Ming-Lang Univ Glasgow Adam Smith Business Sch Glasgow Lanark Scotland Chongqing Univ Coll Mech & Vehicle Engn Chongqing Peoples R China Beijing Univ Technol Coll Econ & Management Res Base Beijing Modern Mfg Dev Beijing Peoples R China Asia Univ Inst Innovat & Circular Econ Taichung Taiwan China Med Univ Hosp Dept Med Res Taichung Taiwan

Purpose The 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/approach This 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. Findings The 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 (R-2). 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/implications The 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/value In prior studies, many scholars have conducted related research on the subject of temperature monitoring. However, this monitoring method can only identify temperature deviations that h

关键词： Cold chain logistics Temperature prediction Extreme learning machine mayfly algorithm

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Synthesis of frequency diverse array with large null depths via a hybrid optimisation approach of mayfly algorithm and convex programing

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IET SIGNAL PROCESSING 2022年第9期16卷 1085-1093页

作者： Shao, Xiaolang Hu, Taiyang Zhang, Jinyu Xiao, Zelong Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Peoples R China

By controlling the null distribution in the range-angle dimension, the frequency diverse array (FDA) can handle range-dependent interference, which is usually difficult to solve for the traditional phased array. However, the current investigations focus more on the design of weights for null control, ignoring the unique design dimension of FDA, that is, the frequency offsets. Therefore, the array potential cannot be fully exploited to maximise the range-dependent interference suppression capability. In this paper, a novel FDA synthesis method with large null depth is proposed. By incorporating the mayfly algorithm and convex programing, frequency offsets and transmit weights are jointly optimised to generate maximum null depths in a given region while maintaining the specified array response of the desired target. Other null control methods for FDA are listed as comparison, including weights-only optimisation method, frequency offsets-only optimisation and frequency offsets and weights sequentially optimisation method. Numerical examples demonstrate the superiority of the proposed algorithm.

关键词： traditional phased array convex programming optimisation range-angle dimension FDA synthesis method hybrid optimisation approach convex programing null distribution null depth frequency offsets-only optimisation null control methods array potential maximum null depths frequency diverse array range-dependent interference suppression capability optimisation method mayfly algorithm specified array response interference suppression unique design dimension array signal processing

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Automatic detection of breast cancer in ultrasound images using mayfly algorithm optimized handcrafted features

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JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2022年第4期30卷 751-766页

作者： Vijayakumar, K. Rajinikanth, V Kirubakaran, M. K. St Josephs Inst Technol Dept Comp Sci & Engn Chennai 600119 Tamil Nadu India St Josephs Coll Engn Dept Elect & Instrumentat Engn Chennai Tamil Nadu India St Josephs Inst Technol Dept Informat Technol Chennai Tamil Nadu India

BACKGROUND: The incidence rates of breast cancer in women community is progressively raising and the premature diagnosis is necessary to detect and cure the disease. OBJECTIVE: To develop a novel automated disuse detection framework to examine the Breast-Ultrasound-Images (BUI). METHODS: This scheme includes the following stages;(i) Image acquisition and resizing, (ii) Gaussian filter-based preprocessing, (iii) Handcrafted features extraction, (iv) Optimal feature selection with mayfly algorithm (MA), (v) Binary classification and validation. The dataset includes BUI extracted from 133 normal, 445 benign and 210 malignant cases. Each BUI is resized to 256x256x1 pixels and the resized BUIs are used to develop and test the new scheme. Handcrafted feature-based cancer detection is employed and the parameters, such as Entropies, Local-Binary-Pattern (LBP) and Hu moments are considered. To avoid the over-fitting problem, a feature reduction procedure is also implemented with MA and the reduced feature sub-set is used to train and validate the classifiers developed in this research. RESULTS: The experiments were performed to classify BUIs between (i) normal and benign, (ii) normal and malignant, and (iii) benign and malignant cases. The results show that classification accuracy of > 94%, precision of > 92%, sensitivity of > 92% and specificity of > 90% are achieved applying the developed new schemes or framework. CONCLUSION: In this work, a machine-learning scheme is employed to detect/classify the disease using BUI and achieves promising results. In future, we will test the feasibility of implementing deep-learning method to this framework to further improve detection accuracy.

关键词： Breast cancer ultrasound image handcrafted features mayfly algorithm binary classification

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