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An Improved Multi Objective mayfly algorithm for Solving Optimal Power Flow Problem Considering Different Loading Conditions

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2022年第10期31卷 2250177-2250177页

作者： Ramesh, S. Bhaskar, K. Vijaya Karunanithi, K. Ettappan, M. Chandrasekar, P. Raja, S. P. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Elect & Elect Engn Chennai 600062 Tamil Nadu India Chennai Inst Technol Dept Elect & Elect Engn Chennai 600069 Tamil Nadu India Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India

This paper presents an improved multi-objective mayfly algorithm (IMOMA) to resolve the optimal power flow (OPF) problem in a regulated power system network with different loading conditions. The OPF problem, considered a multi-objective optimization problem, comprises multiple objective functions related to economic, technical, operational and security aspects. The IMOMA algorithm has been developed by implementing the simulated binary crossover (SBX), polynomial mutation and dynamic crowding distance (DCD) operators in the original multi-objective mayfly algorithm (MOMA).The OPF problem is analyzed by considering multiple objective functions in the IEEE30-bus test system, the IEEE118-bus test system and the 62-bus Indian utility system. The hypervolume performance metric is used to compare the performance of the MOMA and IMOMA with respect to different operating scenarios. Further, loading conditions ranging between 150% and 50% of the base load are considered for the evaluation. The effectiveness of the IMOMA over the MOMA is observed from the results of the different loads. The best compromise solution is obtained from a set of pareto optimal solutions by implementing the TOPSIS method.

关键词： Hypervolume mayfly algorithm multi-objective optimization problem optimal power flow TOPSIS

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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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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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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 Comprehensive Study on a Meta Heuristics Optimization algorithm- Hybridized mayfly algorithm 6

A Comprehensive Study on a Meta Heuristics Optimization Algo...

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6th International Conference on Trends in Electronics and Informatics (ICOEI)

作者： Chandra, B. Ravi Kumar, Krishan LPU Dept Elect & Commun Engg Phagwara Punjab India

ISBN: (纸本)9781665483285;9781665483292

Several optimization techniques have been proposed to solve the optimization problem. This study introduces a novel method known as the mayfly algorithm (MA), which is one of the swarm optimization algorithms. Every problem in the environment has an answer at its core. Based on this, researchers in the present day are preparing one another for the intelligence of nature. Given that the mayfly algorithm (MA) was recently introduced in the year 2020. The proposed algorithm is based on the process of mayflies mating and flying behaviour with the key benefits of both the evolutionary algorithm and swarm optimization. This mayfly algorithm is introduced as a result of the PSO's good hybridization and superior algorithm with the structure of genetic algorithm and firefly algorithm. According to several research studies, the mayfly algorithm outperforms other prominent Metaheuristic algorithms. The exceptional performance of mayfly algorithm is demonstrated by comparing the results in terms of the rate of convergence speed. This article will be a great resource for book reviewers to gain good understanding on the upcoming advanced mayfly algorithm in which a lot of debate has been done on both the sides by analyzing its implementations, applications, and recent advancements for enhancing the proposed mayfly algorithm enforced by the researcher.

关键词： mayfly algorithm Nuptial Dance Multiobjective mayfly algorithm Nature-inspired algorithm Unimodal Multimodal

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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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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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mayfly Sparrow Search Hybrid algorithm for RFID Network Planning

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IEEE SENSORS JOURNAL 2022年第16期22卷 16673-16686页

作者： Zhang, Jiangbo Zheng, Jiali Xie, Xiaode Lin, Zihan Li, Haoshuai Guangxi Univ Sch Comp Elect & Informat Guangxi Key Lab Multimedia Commun & Network Techn Nanning 530004 Guangxi Peoples R China

The radio frequency identification (RFID) technology is frequently used for large-scale RFID network planning, which brings thought-provoking RFID network planning (RNP) problems such as the uncertain number of readers, high interference rate of readers, poor economic benefits, and weak robustness. In this paper, we propose a mayfly sparrow search hybrid algorithm with Circle chaotic map and Levy flight, which is named MASSA. MASSA maps out the position of the readers by adding the idea of the mayfly algorithm (MA) into the optimization process of the sparrow search algorithm (SSA). Circle chaos mapping and Levy flight are added in the SSA optimization process and the nonlinear inertia coefficients to balance the relationship between global search and local search. The MASSA is compared with SSA, Improved sparrow search algorithm (ISSA), MA, particle swarm optimization algorithm (PSO), and gray wolf algorithm (GWO). The first part of the experiment uses the CEC2017 test suite to evaluate algorithm performance and analysis strategy effectiveness. The results encourage the application of MASSA to solve RNP problems. In the second part, experiments were conducted with four objective functions of coverage, interference, power, and load balance. Two different experiments were carried out with fixed number of readers and flexible number of readers under random label conditions. Experimental results show that MASSA has good planning performance, better economy, and robustness.

关键词： Radiofrequency identification Interference Statistics Sociology Optimization Behavioral sciences Planning Radio frequency identification (RFID) RFID network planning (RNP) sparrow search algorithm mayfly algorithm

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