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mayfly Sparrow Search Hybrid algorithm for RFID Network Planning

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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A comparison of swarm-based optimization algorithms in linear antenna array synthesis

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JOURNAL OF COMPUTATIONAL ELECTRONICS 2021年第4期20卷 1520-1531页

作者： Durmus, Ali Kurban, Rifat Karakose, Ercan Kayseri Univ Vocat Coll Dept Elect & Energy Kayseri Turkey Kayseri Univ Vocat Coll Dept Comp Technol Kayseri Turkey Kayseri Univ Engn & Architecture & Design Fac Dept Nat Sci Kayseri Turkey

Today, the design of antenna arrays is very important in providing effective and efficient wireless communication. The purpose of antenna array synthesis is to obtain a radiation pattern with a low side lobe level (SLL) at a desired half power beam width in far-field. The amplitude and position values of the array elements can be optimized to obtain a radiation pattern with suppressed SLLs. In this paper, swarm-based metaheuristic algorithms including particle swarm optimization (PSO), artificial bee colony (ABC), mayfly algorithm (MA) and jellyfish search (JS) are compared to determine the optimal design of linear antenna arrays. Extensive experiments are conducted on designing 10-, 16-, 24- and 32-element linear arrays by determining the amplitude and positions. Experiments are repeated 30 times due to the random nature of swarm-based optimizers, and statistical results show that the performance of the novel algorithms, MA and JS, is better than that of the well-known PSO and ABC methods.

关键词： Linear antenna array synthesis Side lobe level Particle swarm optimization Artificial bee colony algorithm mayfly algorithm Jellyfish search algorithm

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Optimal design of renewable sources of PV/wind/FC generation for power system reliability and cost using MA-RBFNN approach

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2021年第7期45卷 10946-10962页

作者： Ramasamy, Krishnakumar Ravichandran, Coimbatore Subramanian Sri Ramakrishna Engn Coll Dept Elect & Elect Engn Coimbatore Tamil Nadu India

This manuscript proposes a hybrid control method for optimal design of hybrid renewable energy sources (RES) like solar photovoltaic (PV)-wind energy and fuel cell (FC) for power system reliability and cost. The proposed hybrid method is the joint execution of mayfly algorithm (MA) and radial basis function neural network (RBFNN) technique and hence, it is named as MA-RBFNN. Here, the MA-RBFNN algorithm is utilized to minimize the cost function and maximize the reliability of the power system. The major objective of the work is "minimizing the cost under several conditions like, wind, PV and FC and also it reliable for the supply of load, with high reliability and less cost." Here, the MA approach is trained with the inputs viz, the previous instantaneous energy of the obtainable sources, the required reliability, and the related target reference source power. The MA-RBFNN control scheme makes the gain parameters to provide the optimal control signal and manage the energy of the RES. Finally, the proposed MA-RBFNN method gives the optimum control of outage components on reliability with system cost. The MA-RBFNN method is executed in matrix laboratory/Simulink site and the performance is analyzed with different existing methods.

关键词： FC load mayfly algorithm PV radial basis function neural network reliability renewable sources replacement cost WT

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GRaNN: feature selection with golden ratio-aided neural network for emotion, gender and speaker identification from voice signals

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NEURAL COMPUTING & APPLICATIONS 2022年第17期34卷 14463-14486页

作者： Garain, Avishek Ray, Biswarup Giampaolo, Fabio Velasquez, Juan D. Singh, Pawan Kumar Sarkar, Ram Jadavpur Univ Dept Comp Sci & Engn 188 Raja SC Mallick Rd Kolkata 700032 W Bengal India Univ Naples Federico II Dept Math & Applicat Renato Caccioppoli Via Cinthia I-80126 Naples Italy Univ Chile Fac Phys & Math Sci Inst Sistemas Complejos Ingn ISCI Dept Ind Engn Santiago Chile Jadavpur Univ Dept Informat Technol Second CampusPlot 8LB BlockSect 3 Kolkata 700106 W Bengal India

Compared to other features of the human body, voice is quite complex and dynamic, in a sense that a speech can be spoken in various languages with different accents and in different emotional states. Recognizing the gender, i.e. male or female from the voice of an individual, is by all accounts a minor errand for human beings. Similar goes for speaker identification if we are well accustomed with the speaker for a long time. Our ears function as the front end, accepting the sound signs which our cerebrum processes and settles on our disposition. Although being trivial for us, it becomes a challenging task to mimic for any computing device. Automatic gender, emotion and speaker identification systems have many applications in surveillance, multimedia technology, robotics and social media. In this paper, we propose a Golden Ratio-aided Neural Network (GRaNN) architecture for the said purposes. As deciding the number of units for each layer in deep NN is a challenging issue, we have done this using the concept of Golden Ratio. Prior to that, an optimal subset of features are selected from the feature vector extracted, common for all three tasks, from spectral images obtained from the input voice signals. We have used a wrapper-filter framework where minimum redundancy maximum relevance selected features are fed to mayfly algorithm combined with adaptive beta hill climbing (A beta HC) algorithm. Our model achieves accuracies of 99.306% and 95.68% for gender identification in RAVDESS and Voice Gender datasets, 95.27% for emotion identification in RAVDESS dataset and 67.172% for speaker identification in RAVDESS dataset. Performance comparison of this model with existing models on the publicly available datasets confirms its superiority over those models. Results also ensure that we have chosen the common feature set meticulously, which works equally well on three different pattern classification tasks. The proposed wrapper-filter framework reduces the feature dimension s

关键词： Multilayer perceptron Golden ratio mayfly algorithm RAVDESS Gender classification Emotion recognition Speaker identification

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MADCR:Mobility Aware Dynamic Clustering-Based Routing Protocol in Internet of Vehicles

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China Communications 2021年第7期18卷 69-85页

作者： Sankar Sennan Somula Ramasubbareddy Sathiyabhama Balasubramaniyam Anand Nayyar Chaker Abdelaziz Kerrache Muhammad Bilal Deparment of of Computer Science and Engineering Sona College of TechnologySalem 636005India Deparment of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabad 500090India. Graduate School Duy Tan UniversityDa Nang 550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang 550000Vietnam Laboratoire d’Informatique et de Mathématiques UniversitéAmar Telidji de LaghouatLaghouatAlgeria. Department of Computer Engineering Hankuk University of Foreign Studies17035Yongin-SiSouth Korea

Internet of Vehicles(IoV)is an evolution of the Internet of Things(IoT)to improve the capabilities of vehicular ad-hoc networks(VANETs)in intelligence transport *** network topology in IoV paradigm is highly *** is one of the promising solutions to maintain the route stability in the dynamic ***,existing algorithms consume a considerable amount of time in the cluster head(CH)selection ***,this study proposes a mobility aware dynamic clustering-based routing(MADCR)protocol in IoV to maximize the lifespan of networks and reduce the end-to-end delay of *** MADCR protocol consists of cluster formation and CH selection processes.A cluster is formed on the basis of Euclidean *** CH is then chosen using the mayfly optimization algorithm(MOA).The CH subsequently receives vehicle data and forwards such data to the Road Side Unit(RSU).The performance of the MADCR protocol is compared with that ofAnt Colony Optimization(ACO),Comprehensive Learning Particle Swarm Optimization(CLPSO),and Clustering algorithm for Internet of Vehicles based on Dragonfly Optimizer(CAVDO).The proposed MADCR protocol decreases the end-toend delay by 5–80 ms and increases the packet delivery ratio by 5%–15%.

关键词： clustering protocol Internet of things Internet of vehicles optimization algorithm mayfly algorithm

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Cascaded fractional order automatic generation control of a PV-reheat thermal power system under a comprehensive nonlinearity effect and cyber-attack

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ELECTRICAL ENGINEERING 2023年第6期105卷 4339页

作者： Cavdar, Bora Sahin, Erdinc Sesli, Erhan Akyazi, Omur Nuroglu, F. Mehmet Karadeniz Tech Univ Energy Syst Engn TR-61830 Trabzon Turkiye Giresun Univ Comp Engn TR-28200 Giresun Turkiye Karadeniz Tech Univ Elect & Telecommun Engn TR-61830 Trabzon Turkiye Karadeniz Tech Univ Elect & Elect Engn TR-61080 Trabzon Turkiye

The integration of renewable energy systems into the existing power grid has become a global necessity. While there are numerous advantages to this integration, it also presents challenges such as oscillations in system frequency and tie-line power. These issues can lead to instability and undesirable situations within the power system. To ensure a consistent and reliable supply of high-quality electrical power to consumers, a control system is essential. In recent years, cascaded controllers, known for their increased flexibility and degree of freedom compared to non-cascaded controllers, have gained popularity in the literature. In this study, we aim to examine the performance of cascaded fractional and integer order TI-TD and PI-PD controllers in comparison with non-cascaded PID and TID controllers for a challenging stability and robustness assessment. Our focus is on a two-area PV-reheat thermal power system. By conducting comprehensive tests, we aim to gain insights into the effectiveness of these controller types within this context. To ensure optimal controller responses, the designed controllers are optimized using the mayfly algorithm, with a focus on minimizing the integral of the time-weighted absolute error performance index. The performance tests conducted in this study cover various aspects, including time domain analysis, robustness, random load changes, the impact of progressive nonlinearities, and cyber-attack (C-A) scenarios. The power system under examination incorporates several nonlinear components, namely governor dead band, generation rate constraints, boiler dynamics, and time delay. Additionally, two C-A models, namely resonance attack (ResA) and random attack, are applied to the controller systems. Unlike conventional robustness and C-A tests found in existing literature, this study goes further by measuring the response of the proposed controller to high parameter changes (uncertainty) and conducting a quantitative analysis of its resilien

关键词： TI-TD controller PI-PD controller mayfly algorithm Cyber-attack Progressive Nonlinearity effect Random load change Two area PV-thermal reheat power system Robustness and stability toleration limits

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Hybrid railway vehicle trajectory optimisation using a non-convex function and evolutionary hybrid forecast algorithm

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IET INTELLIGENT TRANSPORT SYSTEMS 2023年第12期17卷 2333-2351页

作者： Din, Tajud Tian, Zhongbei Bukhari, Syed Muhammad Ali Mansur Hillmansen, Stuart Roberts, Clive Univ Birmingham Dept Elect Elect & Syst Engn Birmingham England Univ Liverpool Dept Elect Engn & Elect Liverpool England Int Islamic Univ Islamabad Dept Elect & Comp Engn Islamabad Pakistan Univ Birmingham Dept Elect Elect & Syst Engn Birmingham B15 2TT England

This paper introduces a novel optimisation algorithm for hybrid railway vehicles, combining a non-linear programming solver with the highly efficient "mayfly algorithm" to address a non-convex optimisation problem. The primary objective is to generate efficient trajectories that enable effective power distribution, optimal energy consumption, and economical use of multiple onboard power sources. By reducing unnecessary load stress on power sources during peak time, the algorithm contributes to lower maintenance costs, reduced downtime, and extended operational life of these sources. The algorithm's design considers various operational parameters, such as power demand, regenerative braking, velocity and additional power requirements, enabling it to optimise the energy consumption profile throughout the journey. Its adaptability to the unique characteristics of hybrid railway vehicles allows for efficient energy management by leveraging its hybrid powertrain capabilities.

关键词： hybrid railway vehicle mayfly algorithm non-convex optimisation rosenbrock function trajectory optimisation

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A study of diamond grinding wheel wear condition monitoring based on acoustic emission signals

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第9-10期134卷 4367-4385页

作者： Liu, Zihao Chen, Bing Xu, Hu Liu, Guoyue Ou, Wenchu Wu, Jigang Hunan Univ Sci & Technol Coll Mech Engn Hunan Prov Key Lab High Efficiency & Precis Machin Xiangtan 411201 Peoples R China Bichamp Cutting Technol Hunan Co Ltd Changsha 410200 Hunan Peoples R China

The intelligent monitoring of the grinding wheel wear state has the potential to enhance several key aspects of grinding operations, including wheel utilization, wheel dressing, grinding efficiency, grinding quality and so on. In this paper, it is proposed as an acoustic emission signal-based monitoring method of electroplated diamond grinding wheel wear state for C/SiC composite material groove grinding. Firstly, the full-life wear experiment of electroplated grinding wheel grinding C/SiC composites was carried out, and the connection between the acoustic emission signal and the wear state of the grinding wheel was established by frequency domain and time-frequency domain characteristics. Secondly, the time domain, frequency domain and time-frequency domain features of the signals in the stable grinding stage of C/SiC composites were extracted by wavelet packet method. Finally, based on the extracted features, the Extreme Learning Machine (ELM) was optimized by mayfly algorithm (MA) to realize online monitoring and intelligent recognition of grinding wheel wear. The results show that the sample classification accuracy of this method is 96.67%, which can effectively identify the different states of grinding wheel wear.

关键词： Acoustic emission signal Time-frequency-domain analysis Wavelet packet mayfly algorithm Extreme Learning Machine

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Analysis and Usability Evaluation of Virtual Reality in Cultural Landscape Promotion Platform Application

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第10期15卷 95-103页

作者： Huang, Yufang Luo, Yin Zheng, Jingyu Li, Xunxiang Wenzhou Univ Coll Fine Arts & Design Wenzhou 325035 Zhejiang Peoples R China Wenzhou Univ Wenzhou 325035 Zhejiang Peoples R China

order to improve the efficiency of analysing the application of virtual reality technology in cultural landscape promotion platform and enhance the accuracy of usability assessment, a feasibility assessment method based on MA-BiGRU for virtual reality technology cultural landscape promotion platform is proposed. Firstly, it analyses the application of virtual reality technology in cultural landscape promotion platform and designs the application feasibility assessment;secondly, it combines MA algorithm and BiGRU network, and proposes a usability assessment algorithm of virtual reality technology based on MA-BiGRU model;lastly, it analyses the feasibility and validity of the proposed method by using actual cases. The results show that, compared with other models conducted, the proposed method has a higher assessment and prediction accuracy, and it also effectively assists in completing the virtual reproduction of the cultural landscape promotion platform of Dongao Deng Village, Dongtou District, Wenzhou City, and improves the design effect of virtual reality technology in the cultural landscape promotion platform.

关键词： Virtual reality technology cultural landscape promotion platform usability assessment algorithm mayfly algorithm

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Task scheduling to a virtual machine using a multi-objective mayfly approach for a cloud environment

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第24期34卷 e7236-e7236页

作者： Durairaj, Selvam Sridhar, Rajeswari Natl Inst Technol Dept Comp Sci & Engn Tiruchirappalli Tamil Nadu India

Cloud computing has been progressively popular in the arenas of research and business in the recent years. Virtualization is a resource management approach used in today's cloud computing environment. Virtual Machine (VM) migration algorithms allow for more dynamic resource allocation, as well as improvement in computing power and communication capability in cloud data centers. This necessitates an intelligent optimization approach to VM allocation design for an improved performance of application. In this article, a multi-objective optimal design approach is proposed to tackle the tasks of VM allocation. Multi-Objective Optimization (MOO) is a strategy adopted by several methods to handle tasks and workflow scheduling issues that deal with numerous opposing goals. In the cloud computing context, effective task scheduling is critical for achieving cost effective implementation as well as resource utilization. To address the optimal solution, this article proposes an entropy-based multi objective mayfly algorithm is assessed using a convergence pattern in MOO. The model is tested by implementing in a cloud simulator and results prove that the recommended model has an improved performance with regard to factors such as time and utilization rate.

关键词： CDC mayfly algorithm multi-objective optimization task scheduling virtual machine

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