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Robust visual tracking based on modified mayfly optimization algorithm

IMAGE AND VISION COMPUTING 2023年第1期135卷

作者： Xiao, Yuqi Wu, Yongjun West Anhui Univ Sch Mech & Vehicle Engn Luan City 237012 Anhui Peoples R China ChongqingJiaotong Univ Sch Traff & Transportat Chongqing 400074 Peoples R China

In this study, MOA is applied to visual target tracking for the first time, and a novel meta-heuristic tracking algo-rithm with efficiency and precision is obtained. Similar to the common problems of other classical swarm intel-ligence algorithms, standard MOA faces a high probability of falling into local extremals, early maturity anda low efficiency of a late convergence speed. Therefore, super-MOA, a modified MOA method, is proposed in this study. By designing the updating mechanism, the position and velocity parameters of ephemera are monitored and dynamically adjusted with the iteration degree, and the balance of the global and local optimization process in different iteration stages is improved. A mayfly progeny mutation strategy was proposed to alleviate the prema-turity problem of the standard MOA algorithm. Meanwhile, we introduce a chaos algorithm to reconstruct the velocity parameter iteration mechanism, which alleviates the efficiency loss caused by the frequent repeated searching of historical locations by mayflies in standard MOA. In addition, we further understand and improve the parameters such as the dynamic gravitational coefficient and dance coefficient during the courtship flight of mayflies. We also design a frame size adaptive adjustment strategy, which effectively decreases the interfer-ence of invalid features. In terms of experiments, we compare this algorithm with other classical trackers from the qualitative, quantitative and statistical perspectives through OTB2015, VOT2018 and typical large-scale benchmarks. Sufficient tracking experiments in various tracking scenes show that our tracker performs great in terms of efficiency, robustness and accuracy. (c) 2023 Elsevier B.V. All rights reserved.

关键词： Visual tracking mayfly optimization algorithm Scale adaptive tracking frame

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Hyper tuning hybrid MLP-CatBoost classifier using mayfly optimization for detection of heart disease

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HEALTH SERVICES AND OUTCOMES RESEARCH METHODOLOGY 2025年 1-23页

作者： MahaLakshmi, N. Venkata Rout, Ranjeet Kumar Natl Inst Technol Hazratbal Srinagar 190006 Jammu & Kashmir India DR BR Ambedkar Natl Inst Technol Jalandhar Dept IT Jalandhar 144001 Punjab India

Diagnosing heart disease is considered a difficult task as it provides a digital estimation of the seriousness of the disease. As a result, the quickest treatment can be done. So, heart diagnosis has attracted a lot more attention in the medical industry throughout the globe, and along with excellence in efficacy, the optimization algorithm plays a crucial role in the detection of heart disease. Here, to predict the cardiac illness an improvised CatBoost algorithm and Multi-layer Perceptron classifier are used. Also, proper hyperparameter tweaking is needed for the successful implementation of the classifier. In order to optimize the hybrid model's hyperparameters, the mayfly optimization algorithm is deployed for effective hyperparameter optimization. In order to increase prediction accuracy, the Harris-Hawks optimization technique is used to choose the essential features from the dataset. Z-Alizadeh Sani and Cleveland heart disease datasets are utilized to detect heart disease. Also, it is compared with the existing models. To validate the efficiency of a model, six various measures are used: precision, accuracy, recall, the F- 1 measure, specificity, and loss. Here, when compared to the previous studies, the proposed model yields better performance, i.e., 98.7% accuracy with Cleveland and 99.2% with Alizadeh Sani Datasets.

关键词： Machine learning Hyper-parameters tuning optimization algorithm mayfly optimization algorithm Multi-layer perceptron Classification CatBoost algorithm

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Classification of intrusion using Multi-Stream Generative Adversarial Networks in Wireless Sensor Networks

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2025年第2期18卷 1-17页

作者： Prabakar, D. Krishna, Konda Hari Prabhu, D. Femila, L. Karpagam Coll Engn Dept Comp Sci & Engn Coimbatore 641032 Tamilnadu India Mohan Babu Univ Sch Comp Dept Comp Sci & Engn Tirupati 517102 Andhra Pradesh India Vel Tech High Tech Dr Rangarajan Dr Sakunthala Eng Dept Comp Sci & Engn Vel NagarAvadi Chennai 600062 Tamilnadu India St Xaviers Catholic Coll Engn Dept Elect & Commun Engn Chunkankadai 629003 Tamilnadu India

Wireless Sensor Networks (WSNs) are extensively utilized in environmental monitoring, military applications and queue tracking. The short lifespan of sensor nodes are affected by battery depletion, particularly close to the sink node, frequently limits their efficacy and causes energy-holes and node loss. In this paper, an Optimal Cluster Routing Formation and Classification of Intrusion utilizing Multi-Stream Generative Adversarial Networks (OCRF-WSN-MSGAN) is proposed. The proposed approach aims to decrease energy consumption and extend node lifespan while improving intrusion classification. It selects the Cluster Heads (CH) using a fitness function derived from Multi-Stream Generative Adversarial Networks (MSGAN). The system initializes with a model for energy and path selection using Adaptive Tunicate Swarm algorithm (ATSOA). The MSGAN classifies data as normal or abnormal, with its weight parameters optimized by mayfly optimization algorithm (MOA). The proposed OCRF-WSN-MSGAN model is implemented in MATLAB and compared with existing methods under the performance metrics, like network lifetime, number of alive nodes, throughput, packet delivery ratio (PDR) and delay. The proposed OCRF-WSN-MSGAN method attains 20.76%, 26.45%, and 31.79% greater network lifetime, and 19.37%, 16.10%, and 31.23% lesser energy consumption compared with the existing techniques like the construction of an ideal cluster, trusted path for routing and classifying intrusions in WSN using machine learning (OCRF-WSN-RBPDT), combined glow-worm swarm and ant colony optimization for energy effective clustering in WSN(OCRF-WSN-ACI-GSO) and an energy-efficient routing technique utilizing the exponentially-ant lion whale optimization method in WSN(OCRF-WSN-EALWO) respectively.

关键词： Adaptive Tunicate Swarm algorithm mayfly optimization algorithm Multi-Stream Generative Adversarial Networks Wireless Sensor Networks

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Bio-Inspired Intelligent Routing in WSN: Integrating mayfly optimization and Enhanced Ant Colony optimization for Energy-Efficient Cluster Formation and Maintenance

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 127-150页

作者： V.G.Saranya S.Karthik Department of Electronics and Communication Engineering Faculty of Engineering&TechnologySRM Institute of Science and TechnologyVadapalani CampusChennai600026India

Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless *** sensor nodes gather and store data about the real world around ***,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the *** research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and *** present technique employs the hybrid mayfly optimization algorithm-Enhanced Ant Colony optimization(MFOA-EACO),where the mayfly optimization algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony optimization(EACO)technique is used to determine an optimal route between the cluster head and base *** performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy *** objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the *** proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox optimization algorithm(HSFLBOA),Hybrid Social Reindeer optimization and Differential Evolution-Firefly algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider algorithm Differential Evolution(EECHS-ISSADE).

关键词： Enhanced ant colony optimization mayfly optimization algorithm wireless sensor networks cluster head base station(BS)

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An Intelligent Telugu Handwritten Character Recognition Using Multi-Objective mayfly optimization with Deep Learning-Based DenseNet Model

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ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 2023年第3期22卷 1-16页

作者： Sonthi, Vijaya Krishna Nagarajan, S. Krishnaraj, N. Annamalai Univ Dept Comp Sci & Engn FEAT Chidambaram 608002 Tamil Nadu India SRM Inst Sci Technol Sch Comp Dept Networking & Commun Kattankulathur 603203 Tamilnadu India

The handwritten character recognition process has gained significant attention among research communities due to its application in assistive technologies for visually impaired people, human-robot interaction, automated registry for business documents, and so on. Handwritten character recognition of the Telugu language is difficult owing to the absence of a massive dataset and a trained convolutional neural network (CNN). This article introduces an intelligent Telugu character recognition process using a multi-objective mayfly optimization with deep learning (MOMFO-DL) model. The proposed MOMFO-DL technique involves the DenseNet-169 model as a feature extractor to generate a useful set of feature vectors. A functional link neural network (FLNN) is used as a classification model to recognize and classify the printer characters. The design of the MOMFO technique for the parameter optimization of the DenseNet model and FLNN model shows the novelty of the work. The use of MOMFO technique helps to optimally tune the parameters in such a way that the overall performance can be improved. The extensive experimental analysis takes place on benchmark datasets and the outcomes are examined with respect to different measures. The experimental results pointed out the supremacy of the MOMFO technique over the recent state-of-the-art methods.

关键词： Telugu language handwritten character recognition convolutional neural networks parameter tuning deep learning machine learning DenseNet mayfly optimization algorithm

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Quantum mayfly optimization based feature subset selection with hybrid CNN for biomedical Parkinson's disease diagnosis

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NEURAL COMPUTING & APPLICATIONS 2024年第15期36卷 8383-8396页

作者： Mansour, Romany F. New Valley Univ Fac Sci Dept Math El-Kharga 72511 Egypt

Parkinson's disease (PD) arises from brain cell damage and necessitates early detection for effective treatment and symptom management. While various methods such as voice, speech, and written exams have been explored, utilizing automated tools is crucial to enhance accuracy. Recent advancements in artificial intelligence (AI) and deep learning (DL) provide an opportunity for precise early-stage PD identification. This study introduces a novel approach known as Quantum mayfly optimization-based feature subset selection with hybrid convolutional neural network (QMFOFS-HCNN) to improve PD detection and classification. QMFOFS-HCNN is designed to identify optimal feature subsets and overcome the dimensionality challenge. It combines a quantum mayfly optimization approach for feature selection with a convolutional neural network with attention-based long short-term memory for PD detection and classification. Additionally, hyperparameter selection is optimized using the Nadam optimizer. Experimental validation using benchmark datasets yielded compelling results. The QMFOFS-HCNN technique achieved accuracy rates: 96.35% for HandPD Spiral, 96.7% for HandPD Meander, 98.5% for Speech PD, and a perfect 100% for Voice PD datasets. These quantitative findings underscore the potential of AI and DL to enhance early PD detection accuracy significantly. These results offer promising prospects for improving healthcare outcomes in managing PD and related neurological disorders.

关键词： Deep learning Parkinson disease mayfly optimization algorithm Feature selection

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Automated Chronic Obstructive Pulmonary Disease (COPD) detection and classification using mayfly optimization with deep belief network model

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年第PartA期96卷

作者： Dally, E. Christina Rekha, B. Banu SNS Coll Technol Coimbatore India PSG Coll Technol Coimbatore India

Chronic Obstructive Pulmonary Disease (COPD) is a progressive and debilitating respiratory condition affecting millions worldwide. Respiratory disease affects quality of life and poses a substantial economic burden on patients and families. Diagnosis of COPD is unreliable as the test depends on the effort made by the tester and testee. Routine healthcare data collection from patients enables the identification of COPD subtypes so that physicians can define the disease severity and progression. Selecting optimal features from a large volume of healthcare data increases the computation burden and may lead to misclassification. In this research work, the mayfly optimization algorithm is used for optimal feature selection from the COPD Patients Dataset, and the Deep Belief Network is then used for classification. The proposed mayfly Optimized Deep Belief Network (MODBN) performance is experimentally verified using a benchmark dataset, and the performances are comparatively analyzed with traditional machine learning algorithms. A maximum classification accuracy of 96.89 % was attained by the proposed model in the classification of COPD compared to traditional machine learning algorithms.

关键词： Chronic obstructive pulmonary disease Machine learning Deep belief network mayfly optimization algorithm

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Reptile search algorithm and kriging surrogate model for structural design optimization with natural frequency constraints

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MATERIALS TESTING 2022年第10期64卷 1504-1511页

作者： Yildiz, Betul Sultan Bureerat, Sujin Panagant, Natee Mehta, Pranav Yildiz, Ali Riza Bursa Uludag Univ Dept Mech Engn TR-16059 Bursa Turkey Khon Kaen Univ Dept Mech Engn Khon Kaen Thailand Dharmsinh Desai Univ Dept Mech Engn Nadiad 387001 Gujarat India

This study explores the use of a recent metaheuristic algorithm called a reptile search algorithm (RSA) to handle engineering design optimization problems. It is the first application of the RSA to engineering design problems in literature. The RSA optimizer is first applied to the design of a bolted rim, which is constrained optimization. The developed algorithm is then used to solve the optimization problem of a vehicle suspension arm, which aims to solve the weight reduction under natural frequency constraints. As function evaluations are achieved by finite element analysis, the Kriging surrogate model is integrated into the RSA algorithm. It is revealed that the optimum result gives a 13% weight reduction compared to the original structure. This study shows that RSA is an efficient metaheuristic as other metaheuristics such as the mayfly optimization algorithm, battle royale optimization algorithm, multi-level cross-entropy optimizer, and red fox optimization algorithm.

关键词： battle royale optimization algorithm brake pedal engineering structures mayfly optimization algorithm multi-level cross-entropy optimizer optimization red fox optimization algorithm reptile search algorithm

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Stability Enhancement of Sulselrabar Electricity System Using mayfly algorithm Based on Static Var Compensator and Multi-Band Power System Stabilizer PSS2B

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IEEE ACCESS 2023年 11卷 57319-57340页

作者： Djalal, Muhammad Ruswandi Robandi, Imam Prakasa, Mohammad Almas Sepuluh Nopember Inst Technol Dept Elect Engn Surabaya 60111 Indonesia

An additional controller in an electric power system is currently required to increase the system stability, especially when a disturbance occurs. The stability of the multimachine system can be increased by installing a Static Var Compensator (SVC) and Power System Stabilizer (PSS). However, SVC and PSS equipment require precise coordination to determine the optimal location and parameters. This study presents an optimal analysis of SVC coordination with single-band PSS1A and multi-band PSS2B (MB-PSS2B) in the South, Southeast and West Sulawesi (Sulselrabar) electrical systems. An artificial intelligence method based on the mayfly optimization algorithm (MOA) is proposed to optimize the location and parameters of the SVC and PSS. A comparative investigation related to controller parameter optimization from a previous work was used to measure the effectiveness of the MOA based on the Firefly algorithm (FA) and Particle Swarm optimization (PSO). Performance analysis using the time-domain simulation method to review the speed deviation response, field voltage response, and rotor angle response for each generator, as well as eigenvalue analysis for each control scheme when there is a change in the load disturbance on generators 1 and 13. The results show an increase in bus voltage from critical to marginal conditions and a decrease in network losses after installing SVC on bus 31 of 40 MW capacity. The application of MB-PSS2B based on the MOA provided an increased damping ratio, optimal speed response, rotor angle, field voltage generator, and eigenvalue system after installing 14 PSS.

关键词： Stability SVC multi-band PSS2B mayfly optimization algorithm eigenvalue

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Prediction of compressive strength of high-performance concrete via coupled meta-heuristic random forest regression techniques

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第2期7卷 931-945页

作者： Liu, Lei Du, Bingxuan Yu, Kan Wei, Wei Hebei Bldg Mat Vocat & Tech Coll Qinhuangdao 066000 Peoples R China

High-performance concrete (HPC) stands as a revolutionary material in construction. Its composition comprises fundamental components: water, cement, fine and coarse aggregates, and various additives. These constituent ratios significantly influence the ultimate compressive strength (CS) of HPC structures, elevating the importance of accurate CS prediction in their design. Beyond design, precise CS forecasting carries implications for quality control, cost efficiency, and time management in construction projects. Recent research trends emphasize the development of efficient predictive techniques that alleviate the need for extensive time, labor, and costly experiments. The burgeoning interest in leveraging machine learning methodologies for estimating diverse material properties underscores this shift. This study aims to assess the robustness and accuracy of hybrid models integrating Random Forest Regression(RFR) with three meta-heuristic optimizers: Coronavirus Herd Immunity Optimizer (CHRO), mayfly optimization algorithm (MOA), and Fitness Dependent Optimizer (FDO) for CS prediction in HPC. This approach splits the dataset into 70% training and 30% testing models. Several evaluation metrics, including R2, RMSE, MAE, RAE, and Tstate, are employed to gauge the precision and efficacy of these models. A promising model is ultimately identified for precise CS predictions under practical scenarios. This work contributes to optimizing HPC design and construction processes, offering insights into material behavior and performance.

关键词： High-performance concrete Compressive strength Random forest regression Coronavirus herd immunity optimizer mayfly optimization algorithm Fitness-dependent optimizer

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