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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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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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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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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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Optimization of hydraulic parameters for pipeline system of hydropower station with super long headrace tunnel based on mayfly algorithm considering operational scenarios

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ENERGY SCIENCE & ENGINEERING 2024年第6期12卷 2341-2364页

作者： Wang, Le Guo, Wencheng Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China

This paper studies the optimization of hydraulic parameters for pipeline system of hydropower station with super long headrace tunnel (HSSLHT) based on mayfly algorithm considering operational scenarios. Firstly, the state equation of HSSLHT under load disturbance is derived. The optimization design of hydraulic parameters for pipeline system based on mayfly algorithm is proposed. Then, the optimization of hydraulic parameters is conducted and analyzed. Finally, the effects of pipeline diameters and transfer coefficients of turbines on the optimization of hydraulic parameters for pipeline systems are revealed. The results show that the optimization of hydraulic parameters for pipeline system is a multiobjective problem, and the several objective functions exhibit significant conflicts. Compared to the firefly algorithm and genetic algorithm, the objective function under mayfly algorithm is improved by 14.7% and 5.1%, respectively. The mayfly algorithm can make the hydraulic parameters for the pipeline system reach the Pareto optimal solution under both load decrease condition and load increase condition. The diameter of penstock has an obvious influence on the robustness of dynamic performance of HSSLHT. When the diameter of penstock increases by 30%, the robustness of HSSLHT becomes worse and the robustness index deteriorates by 57%. The reason is that the flow inertia of penstock becomes smaller with the increase of diameter, and the flow inertia of penstock is favorable for resisting disturbance of HSSLHT. The coefficient of throttled orifice head loss and the lengths and head losses of headrace tunnel and penstock are the key hydraulic parameters for matching the operation of HSSLHT. This paper studies the optimization of hydraulic parameters for pipeline system of hydropower station with super long headrace tunnel (HSSLHT) based on mayfly algorithm considering operational scenarios. Firstly, the state equation of HSSLHT under load disturbance is derived. The op

关键词： hydraulic parameters hydropower station mayfly algorithm multiobjective optimization pipeline system

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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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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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COLMA: a chaos-based mayfly algorithm with opposition-based learning and Levy flight for numerical optimization and engineering design

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JOURNAL OF SUPERCOMPUTING 2023年第17期79卷 19699-19745页

作者： Zhao, Yanpu Huang, Changsheng Zhang, Mengjie Lv, Cheng China Univ Petr East China Sch Econ & Management Qingdao 266580 Shandong Peoples R China Dareway Software Co Ltd Jinan 250200 Shandong Peoples R China

The mayfly algorithm (MA) is a widely used metaheuristic algorithm characterized by a simple structure with simple parameters. However, MA may have problems such as poor global search ability and tend to fall into local optima. To overcome these limitations, this paper presents a chaos-based mayfly algorithm with opposition-based learning and Levy flight (COLMA) to boost the global search and local exploitation performance. In COLMA, we first introduced tent chaos to optimize the initialization process of the mayfly population, as random initialization processes may result in low diversity of the mayfly population. In addition, the gravity coefficient has been replaced with an adaptive gravity coefficient to balance the global search ability and local exploitation ability during the iterative process of the algorithm. In the process of updating the position of the male mayfly population, an opposition-based learning strategy based on an iterative chaotic map with infinite collapses is adopted to prevent the male mayfly population from falling into local optima. At the same time, in order to solve the problem of small search range of female mayfly population, Levy flight strategy was introduced to replace random walk strategy. Finally, an offspring optimization strategy was proposed to increase the probability of the offspring mayfly population approaching the optimal solution. To verify the effectiveness and superiority of COLMA and the adopted strategy, experiments were conducted on the classical benchmark functions, CEC 2017 benchmark suite and CEC 2020 real-world constraint optimization problems, and the results were statistically tested using the Wilcoxon signed rank test and Friedman test. The analysis results show that the proposed COLMA has statistical validity and reliability and has great advantages compared with MA, variant MA in terms of optimization accuracy, stability and convergence speed.

关键词： mayfly algorithm Metaheuristic algorithm Levy flight Opposition-based learning Numerical optimization Engineering design

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An improved hybrid mayfly algorithm for global optimization

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JOURNAL OF SUPERCOMPUTING 2023年第6期79卷 5878-5919页

作者： Yan, Zheping Yan, Jinyu Wu, Yifan Zhang, Chao Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China

As the complexity of optimization problems increases, metaheuristic algorithms play an important role in dealing with complex computational problems and try to find the best solution from all feasible solutions to the problem. The mayfly algorithm is a novel metaheuristic algorithm based on the social behavior of biological groups. The algorithm achieves global and local search by simulating the flight behavior and mating process of mayflies to obtain global optimal solution. However, the traditional mayfly algorithm has problems such as low convergence accuracy and poor stability and is prone to becoming trapped in local optimality. Aiming at the problem of the mayfly algorithm, an improved mayfly algorithm combined with the gray wolf optimization algorithm (MA-GWO) is proposed. In the mayfly algorithm, the Levy flight strategy and the hunting mechanism of the gray wolf optimization algorithm are introduced to achieve complementary advantages. To verify the superiority of the proposed algorithm, 19 classical benchmark functions, CEC-C06 2019 test functions and 5 engineering design problems are compared with various advanced metaheuristic algorithms. The experimental data show that the MA-GWO algorithm has significant enhancements over the traditional mayfly algorithm. In several test cases, especially for high-dimensional optimization problems, the MA-GWO algorithm is far superior to other metaheuristic algorithms, has better convergence and stability, and is an effective and feasible algorithm.

关键词： mayfly algorithm Levy flight strategy Grey wolf optimization Benchmark test functions Metaheuristic algorithms

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Android Malicious Application Detection Based on Improved mayfly algorithm 22

Android Malicious Application Detection Based on Improved Ma...

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IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) / BigDataSE Conference / CSE Conference / EUC Conference / ISCI Conference

作者： Wei, Yinzhen Lu, Shuo Wuhan Text Univ Sch Comp Sci & Artificial Intelligence Wuhan Peoples R China Wuhan Vocat Coll Software & Engn Huanggang Normal Coll Wuhan Peoples R China

ISBN: (纸本)9798350381993;9798350382006

With the rapid development of the Internet and mobile terminals, Android is one of the most popular mobile operating systems. However, the proliferation of Android malicious applications is also very serious, so it is necessary to detect and deal with Android applications in advance, and how to make effective selection among many features is a crucial process in malicious application detection. In this paper, an Android malicious application detection model is established based on the improved mayfly algorithm with reference to related Android malicious detection methods for Android platform applications. By effectively selecting the features, the optimal combination of features is obtained to optimize the classification results, so as to improve the detection performance of Android malicious application *** static analysis is used to extract the features of Android applications, and the malicious application detection model is examined by various classification algorithms, and the results confirm the feasibility and superiority of the proposed Android malicious application detection method based on the improved mayfly algorithm.

关键词： Malicious Application Detection mayfly algorithm Feature Selection Feature Optimization

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