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A parallel mayfly algorithm for the a-neighbor p-center problem

APPLIED SOFT COMPUTING 2023年 144卷

作者： Cura, Tunchan Istanbul Univ Fac Business Adm Istanbul Turkiye

This study presents a competitive algorithm in which the mayfly optimization algorithm (MA) is combined with a local search procedure to provide high-quality solutions to the & alpha;-neighbor p-center problem (& alpha;- pCP). The aim of this problem is to locate p facilities on a plane such that the maximum distance between each demand point and its & alpha;th closest facility is minimized. Although & alpha;- pCP is a discrete optimization problem, it is addressed in this study from the perspective of a continuous optimization problem. The major reason for this choice is that the MA is a very suitable technique for finding the optimum point on a plane. However, it is very difficult to successfully apply this technique to a discrete optimization problem. Hence, additional efforts are made to reduce the computational burden that arises from approaching this from the perspective of a continuous problem. Thus, an effective solution method based on the MA for finding the optimum point on a plane is presented for the discrete & alpha;- pCP. The proposed MA method is compared to the best alternatives found in the literature, which are an extremely efficient exact procedure for the continuous variant of the problem, and a GRASP algorithm that includes a tabu search with strategic oscillation (SO) post-processing. A comparative analysis shows that the performance of the proposed algorithm is similar to that of these state-of-the-art methods. A more extensive comparison shows that the two best performing algorithms, the MA and the SO, complement each other. In addition, the proposed method performs very well for cases where the number of facilities are low. If we consider only the cases where p & LE;50, we see that the MA performs best 5 times, 14 times and 14 times for & alpha;= 1, 2, 3, respectively, and loses twice, three times, and three times.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Metaheuristics mayfly algorithm Local search Search-in-grid strategy p-center problem

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Bioinspired Bare Bones mayfly algorithm for Large-Scale Spherical Minimum Spanning Tree

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FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2022年 10卷 830037页

作者： Zhang, Tian Zhou, Yongquan Zhou, Guo Deng, Wu Luo, Qifang Guangxi Univ Nationalities Coll Artificial Intelligence Nanning Peoples R China Guangxi Key Labs Hybrid Computat & IC Design Anal Nanning Peoples R China China Univ Polit Sci & Law Dept Sci & Technol Teaching Beijing Peoples R China Civil Aviat Univ China Coll Elect Informat & Automat Tianjin Peoples R China

mayfly algorithm (MA) is a bioinspired algorithm based on population proposed in recent years and has been applied to many engineering problems successfully. However, it has too many parameters, which makes it difficult to set and adjust a set of appropriate parameters for different problems. In order to avoid adjusting parameters, a bioinspired bare bones mayfly algorithm (BBMA) is proposed. The BBMA adopts Gaussian distribution and Levy flight, which improves the convergence speed and accuracy of the algorithm and makes better exploration and exploitation of the search region. The minimum spanning tree (MST) problem is a classic combinatorial optimization problem. This study provides a mathematical model for solving a variant of the MST problem, in which all points and solutions are on a sphere. Finally, the BBMA is used to solve the large-scale spherical MST problems. By comparing and analyzing the results of BBMA and other swarm intelligence algorithms in sixteen scales, the experimental results illustrate that the proposed algorithm is superior to other algorithms for the MST problems on a sphere.

关键词： mayfly algorithm bare bones mayfly algorithm large-scale spherical MST Prufer code bioinspired algorithm

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Short-Term Power Prediction of a Wind Farm Based on Empirical Mode Decomposition and mayfly algorithm-Back Propagation Neural Network

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Gong, Zeweiyi Ma, Xianlong Xiao, Ni Cao, Zhanguo Zhou, Shuai Wang, Yaolong Guo, Chenjun Yu, Hong Elect Power Res Inst Yunnan Power Grid Co Ltd Kunming Peoples R China Kunming Univ Sci & Technol Oxbridge Coll Kunming Peoples R China

With the improvement of energy consumption structure, the installed capacity of wind power increases gradually. However, the inherent intermittency and instability of wind energy bring severe challenges to the dispatching operation. Wind power forecasting is one of the main solutions. In this work, a new combined wind power prediction model is proposed. First, a quartile method is used for data cleaning, namely, identifying and eliminating the abnormal data. Then, the wind power data sequence is decomposed by empirical mode decomposition to eliminate non-stationary characteristics. Finally, the wind generator data are trained by the MA-BP network to establish the wind power prediction model. Also, the simulation tests verify the prediction effect of the proposed method. Specifically speaking, the average MAPE is decreased to 12.4979% by the proposed method. Also, the average RMSE and MAE are 107.1728 and 71.604 kW, respectively.

关键词： wind power short-term prediction empirical mode decomposition BP neural network mayfly algorithm renewable energy

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Discrete mayfly algorithm for spherical asymmetric traveling salesman problem

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 221卷

作者： Zhang, Tian Zhou, Yongquan Zhou, Guo Deng, Wu Luo, Qifang Guangxi Univ Nationalities Coll Artificial Intelligence Nanning 530006 Peoples R China China Univ Polit Sci & Law Dept Sci & Technol Teaching Beijing 102249 Peoples R China Civil Aviat Univ China Coll Elect Informat & Automat Tianjin 300300 Peoples R China Guangxi Key Labs Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China

The Asymmetric Traveling Salesman Problem (ATSP), as a branch of the traveling salesman problem, aims to find a group of circuits when a salesman travels all cities at the minimum cost and returns to the starting city under the condition of different round-trip costs between the two cities. mayfly algorithm (MA) is a swarm-based metaheuristic algorithm, which has been applied to many fields successfully. In this paper, we arrange all cit-ies on the sphere. A Discrete mayfly algorithm (DMA) for solving spherical asymmetric traveling salesman problem is proposed. The algorithm applies inver-over operator, crossover operator and 3-opt operator to simplify the parameters of the algorithm, enrich the diversity of the population and enhance the local search ability. By experimenting, analyzing and comparing the effects of DMA and other metaheuristic algorithms in twelve scales, this study proves that the proposed algorithm is superior to others for the spherical ATSP, espe-cially in high-dimensional cases.

关键词： mayfly algorithm Spherical ATSP Inver -over operator 3-opt operator Metaheuristic

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Multi-Objective Optimization with mayfly algorithm for Periodic Charging in Wireless Rechargeable Sensor Networks

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WORLD ELECTRIC VEHICLE JOURNAL 2022年第7期13卷 120页

作者： Mukase, Sandrine Xia, Kewen Hebei Univ Technol Sch Elect & Informat Engn Tianjin 300401 Peoples R China

Wireless energy transfer (WET) is a revolutionary method that has the power to tackle the energy and longevity challenges in wireless sensor networks (WSN). This paper uses a mobile charger (MC) to discover the procedure of WET based on a wireless sensor network (WSN) for a periodic charging technique to maintain the network operational. The goal of this work is to lower overall system energy consumption and total distance traveled while increasing the mobile charger device vacation time ratio. Based on an analysis of total energy consumption, a new metaheuristic called mayfly algorithm (MA) is used to achieve energy savings. Instead of charging all nodes at the same time in each cycle, in our strategy, the mobile charger charges only energy-hungry nodes due to their levels of energy. In this strategy, when the first node reaches the calculated minimum energy, it notifies the base station (BS), which computes all nodes that fall under threshold energy and sends the MC to charge all of them to the maximum energy level in the same cycle. Mathematical results show that the mayfly algorithm can considerably decrease the charging device's total energy consumption and distance traveled while maintaining performance because it can keep the network operational with less complexity than other schemes.

关键词： energy consumption mayfly algorithm wireless energy transfer periodic charging wireless renewable sensor networks

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Tuberculosis detection in chest X-ray using mayfly-algorithm optimized dual-deep-learning features

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JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2021年第6期29卷 961-974页

作者： Rajakumar, M. P. Sonia, R. Maheswari, B. Uma Karuppiah, S. P. St Josephs Coll Engn OMR Chennai 600119 Tamil Nadu India BS Abdur Rahman Crescent Inst Sci & Technol Chennai Tamil Nadu India

World-Health-Organization (WHO) has listed Tuberculosis (TB) as one among the top 10 reasons for death and an early diagnosis will help to cure the patient by giving suitable treatment. TB usually affects the lungs and an accurate bio-imaging scheme will be apt to diagnose the infection. This research aims to implement an automated scheme to detect TB infection in chest radiographs (X-ray) using a chosen Deep-Learning (DL) approach. The primary objective of the proposed scheme is to attain better classification accuracy while detecting TB in X-ray images. The proposed scheme consists of the following phases namely, (1) image collection and pre-processing, (2) feature extraction with pre-trained VGG16 and VGG19, (3) mayfly-algorithm (MA) based optimal feature selection, (4) serial feature concatenation and (5) binary classification with a 5-fold cross validation. In this work, the performance of the proposed DL scheme is separately validated for (1) VGG16 with conventional features, (2) VGG19 with conventional features, (3) VGG16 with optimal features, (4) VGG19 with optimal features and (5) concatenated dual-deep-features (DDF). All experimental investigations are conducted and achieved using MATLAB (R) program. Experimental outcome confirms that the proposed system with DDF yields a classification accuracy of 97.8% using a K Nearest-Neighbor (KNN) classifier.

关键词： Tuberculosis chest X-ray VGG16 VGG19 mayfly algorithm dual-deep-features

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An improved MPPT method for photovoltaic systems based on mayfly optimization algorithm

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ENERGY REPORTS 2022年 8卷 141-150页

作者： Mo Shixun Ye Qintao Jiang Kunping Mo Xiaofeng Shen Gengyu Guangxi Univ Sch Elect Engn Nanning 530000 Guangxi Peoples R China

Because the traditional maximum power point tracking (MPPT) of photovoltaic (PV) array is easy to fail in complex environment, and the existing intelligent optimization algorithms have shortcomings such as slow convergence speed, low convergence precision and poor stability. Therefore, this paper proposes a new intelligent optimization algorithm MPPT tracking method. Based on mayfly intelligent optimization algorithm, this method can track the maximum power point (MPP) of PV system by two populations, which can quickly and accurately track MPP of PV system under complex environmental conditions. By using Matlab/Simulink simulation platform, the method was studied under uniform illumination and non-uniform illumination respectively, and its effectiveness was verified. Compared with the particle swarm optimization algorithm (PSO), its superiority is verified. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 2021 The 2nd International Conference on Power Engineering, ICPE, 2021.

关键词： Photovoltaic array Maximum power tracking mayfly algorithm Local shading MATLAB/SIMULINK

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A mayfly optimization algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2020年 145卷 106559-106559页

作者： Zervoudakis, Konstantinos Tsafarakis, Stelios Tech Univ Crete Sch Prod Engn & Management Khania Greece

This paper introduces a new method called the mayfly algorithm (MA) to solve optimization problems. Inspired from the flight behavior and the mating process of mayflies, the proposed algorithm combines major advantages of swarm intelligence and evolutionary algorithms. To evaluate the performance of the proposed algorithm, 38 mathematical benchmark functions, including 13 CEC2017 test functions, are employed and the results are compared to those of seven state of the art well-known metaheuristic optimization methods. The MA's performance is also assessed through convergence behavior in multi-objective optimization as well as using a real-world discrete flow-shop scheduling problem. The comparison results demonstrate the superiority of the proposed method in terms of convergence rate and convergence speed. The processes of nuptial dance and random flight enhance the balance between algorithm's exploration and exploitation properties and assist its escape from local optima.

关键词： mayfly algorithm Nature-inspired algorithm Optimization

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Neighborhood Rough Set Reduction with Improved mayfly Optimization algorithm

Neighborhood Rough Set Reduction with Improved Mayfly Optimi...

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2021中国智能自动化大会(CIAC 2021)

作者： Lijuan Shi Zhou Feng Yiyu Sang Xinlin Xie Xinying Xu College of Electrical and Power Engineering Taiyuan University of Technology School of Electronic Information Engineering Taiyuan University of Science and Technology

Attribute reduction refers to selecting some of the most effective attributes from the original attribute sets to reduce the dimensionality of the dataset and optimize the attribute indicators of the system. Rough set theory has made good achievements in attribute reduction as dataset preprocessor, but the current methods are not enough to find the global best subset. The mayfly algorithm is a new intelligent optimization algorithm, which mainly includes group gathering,crossing, mutation, wedding dance and random walking. In this paper, an attribute reduction algorithm of neighborhood rough set based on improved MA algorithm is proposed. Two Sigmoid mapping functions are used to binary encoding of the mayfly, the switching conditions of the two positions update strategies are set to control migration trend distribution of the mayfly. And then the neighborhood rough set is combined to find the best attribute subset. The UCI and OpenML datasets are used as experiment datasets. The experimental results verify the superiority of the proposed method in finding an optimal reduction. At the same time,the proposed method has higher classification accuracy and faster convergence speed.

关键词： Attribute reduction Rough set theory mayfly algorithm Position update strategy

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A novel cascade control of PEMFC: Regulation of stack voltage and air breathing subsystem

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2025年 102卷 1530-1545页

作者： Panjwani, Bharti Yadav, Jyoti Kumar, Pankaj Panjwani, Bhawna Mohan, Vijay Shri Madhwa Vadiraja Inst Technol & Management Dept Comp Sci & Engn Bantakal Karnataka India Netaji Subhas Univ Technol Dept ICE Sect 3 Dwarka New Delhi India Manipal Acad Higher Educ Manipal Inst Technol Dept Informat & Commun Technol Udupi 576104 Karnataka India Indian Inst Technol Ctr Interdisciplinary Programs Hyderabad India Manipal Acad Higher Educ Manipal Inst Technol Dept Mechatron Udupi 576104 Karnataka India

The proton exchange membrane fuel cell (PEMFC) stack is an excellent source of clean energy for electric vehicles with zero carbon footprint. The abrupt changes in power demand in automotive applications can adversely impact the oxygen excess ratio (OER) and stack voltage. Therefore, this study aims to design an intelligent cascade control framework to ensure the durable and efficient operation of PEMFC. A novel mixed locally recurrent fuzzy neural network proportional-integral-derivative (RFNN-PID) control architecture is proposed. The RFNN-PID and proportional-integral (PI) controllers are employed as the primary and secondary controllers in cascaded loops, respectively. Four other cascaded schemes with fuzzy PID and PI (FPID/PI), two-degree-of- freedom PID & two-degree-of-freedom PI (2DOF-PID/2DOF-PI), fractional order PID & fractional order PI (FOPID/FOPI), and PID/PI controllers are designed for comparison. The controller parameters are optimised using the mayfly algorithm (MA) to minimise the integral time absolute error (ITAE). The findings suggest the cascaded RFNN-PID/PI strategy yields the fastest voltage regulation and minimum ITAE (0.3018 s & 3.406), as compared to cascaded FPID/PI (0.4087 s & 4.273), 2DOF-PID/2DOF-PI (0.4213 s & 6.626), FOPID/FOPI (0.8135 s & 9.083) and PID/PI (1.066 s & 10.89). The proposed controller also exhibits robust performance in the presence of disturbance and noise. Hence, it is concluded that the adaptive RFNN-PID/PI controller efficiently controls the voltage and regulates OER even in adverse operating conditions.

关键词： Hydrogen energy Proton exchange membrane fuel cell Recurrent fuzzy neural network Fuzzy control mayfly algorithm

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