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检索条件"主题词=Whale optimization algorithms"

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An Improved Resource Allocation in 5G C-RAN using Hybrid Swarm Intelligence Algorithm

An Improved Resource Allocation in 5G C-RAN using Hybrid Swa...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Voore Subba Rao G. Ravindra Babu Department of CSE APJ Abdul Kalam Technical University in Lucknow India Department of CSE Swami Vivekanand Subharti University Meerut India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

C-RAN is known as Cloud Radio Access Network is a centralized Radio Access Network is an architecture specialized design for radio access network for co-ordinate 2G, 3G,4G and 5G mobile communications for future wireless communications standards. In C-RAN architecture all BBUs are include in base stations will be connected to a Baseband Unit of a centralized pool.. The frequence known as RD is in-expensive RRH are in C-RAN architecture. The Basseband Units are efficient computational tasks are consuming more energy. It is essential to minimize energy consumption and maximize energy efficiency. The resource allocation is a strategy to satisfy Quality of Services, and balance energy efficiency this paper proposed a hybrid method of dynamic resource allocation management algorithm ITS-WOA(Improved Tunicate Swarm whale optimization) to minimize the energy consumption of Baseband Units and also minimize energy consumption by Remote Radio Access. The optimization method is to minimize usage of RRH mapping with BBU and deployed need for RRH and BBU deployment to minimize the operational cost. There should be a provision of active and sleep mode of selection for the both RRH and BBU to minimize energy. The active state shows BBU and RRH are in running state. And sleep mode state shows BBU and RRH are in sleep mode to minimize the energy consumption and maximize the energy utilization for the maintenance of Quality of Service in C-RAN architecture.

关键词： Energy consumption Costs Baseband Heuristic algorithms Computer architecture Quality of service whale optimization algorithms Energy efficiency Resource management Particle swarm optimization

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Research on Bridge Pier Crack Feature Image Recognition and Classification Method Based on IGOA-SVM

Research on Bridge Pier Crack Feature Image Recognition and ...

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Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Yueyue Zong Jiahui Ye Zhiqiang Liu Feng Zhou School of Mechanical Engineering Jiangsu University of Science and Technology Zhenjiang China Marine Engineering and Electromechanical Research Laboratory Zhejiang Marine Development Research institute Zhoushan China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

In response to the issues of low automation, strong subjectivity in detection results, and serious impacts on the safety of bridges and public outings caused by existing methods for detecting cracks in bridge piers, this paper proposes an improved grasshopper optimization algorithm (IGOA) to optimize a support vector machine (SVM) classification model for crack detection. First, feature extraction of crack images is performed using a threshold segmentation method. Then, interference information such as surface impurities is removed using a binarization inverse operation and noise removal algorithm. The IGOA-SVM model is then employed to identify and classify the crack images of bridge piers. Finally, the accuracy and precision of the classification results are analyzed and compared among the SVM, grasshopper optimization algorithm (GOA)-SVM, and IGOA-SVM models. The results indicate that the proposed IGOA-SVM model improves both accuracy and precision, providing a new approach for the accurate identification of cracks in bridge piers.

关键词： Support vector machines Bridges Accuracy Data models whale optimization algorithms Classification algorithms Safety Surface cracks Particle swarm optimization optimization

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A Cooperative Method for Solving the Set-Union Knapsack Problem

A Cooperative Method for Solving the Set-Union Knapsack Prob...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Juntao Zhao Mhand Hifi College of Information Science and Engineering Northeastern University Shenyang China EPROAD UR 4669 UPJV Amiens France Laboratory EPROAD UR 4669 UPJV Amiens France

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

In this paper, a population-based method, along with various local operators, is proposed to tackle the set-union knapsack problem. The devised approach integrates diverse features to form a customized cooperative method. It includes a greedy procedure for generating an initial set of solutions, an optimized greedy repair and optimization operator to handle infeasibility, a tailored local operator for exploring the search space, and the integration of a multi-neighborhood tabu search operator to enhance the global best solution. These operators are integrated into an iterative search based on the whale optimization procedure, maintaining a focus on solution quality throughout the process. Finally, an experimental study is presented to evaluate the proposed method’s performance on benchmark instances extracted from the literature. The provided results are compared with those achieved by more recent methods available in the literature, highlighting the efficiency of the method and revealing several new solutions.

关键词： Process control Machine learning Maintenance engineering Benchmark testing whale optimization algorithms Space exploration Iterative methods Information technology optimization Mathematical programming

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Denial of Service (DoS) Attack Detection Using Feed Forward Neural Network in Cloud Environment

Denial of Service (DoS) Attack Detection Using Feed Forward ...

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Data Science and Network Security (ICDSNS), International Conference on

作者： Ponugoti Kalpana P. Srilatha Gudepu Sai Krishna Ahmad Alkhayyat Debarshi Mazumder Department of Computer Science and Engineering AVN Institute of Engineering and Technology Hyderabad India Department of AI & DS Chaitanya Bharathi Institute of Technology Hyderabad India Department of Information Technology Anurag University Hyderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

In the recent years, the Denial of Service (DoS) developed as a big security threats for networks. Preventing as well as identifying DoS attacks is a big task within the industries. There are IoT of existing methods are there to identify DoS attacks and minimize the damage. However, many of these methods do not efficiently distinguish noise from a signal. This research focuses on addressing the DoS problem and introduces a method called Feed forward Neural Network (FNN). First, the KDD cup 99 data set goes through a preprocessing step where data normalization is processed using Min-Max normalization which standardizes the data points values. This normalized data is then passed to the whale optimization method which selects the relevant features making the classification system easier. The selected features are then used by an FNN classifier to differentiate between normal and attacked data. The proposed method is implemented and simulated on MATLAB and tested experimentally resulting to a detection accuracy of 0.99. The proposed FNN classifier is compared with the existing classifiers such as Auto-encoder and LSTM where it outperformed in detecting DoS attacks efficiently.

关键词： Industries Accuracy Neural networks Noise optimization methods Network security whale optimization algorithms Mathematical models Feeds Matlab

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Research on Infrared Thermal Imaging Zero-value Insulator Identification Based on WOA-SVM Algorithm

Research on Infrared Thermal Imaging Zero-value Insulator Id...

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Electrical Engineering and Mechatronics Technology (ICEEMT), IEEE International Conference on

作者： Haolin Li Song Yang Xiaolin Wang Lifeng Jia Wang Yong Xinqiang Wu Li Long Liu Chang Baishan Power Supply Company State Grid Jilin Electric Power Co. Ltd Baishan China

ISBN: (数字)9798350356472

ISBN: (纸本)9798350356489

Aiming at the problem that porcelain insulators are subjected to the influence of electromechanical loads and adverse weather conditions for a long period of time during operation, which leads to their deterioration and the occurrence of zero-value faults. In this study, an infrared thermal imaging zero-value insulator fault identification and diagnosis technique based on the combination of whale optimization Algorithm (WOA) and binary support vector machine (SVM) classifiers is proposed to determine whether a zero-value fault occurs. Firstly, the infrared image of porcelain insulator is preprocessed to obtain the target feature information of zero-value insulator, and the zero-value insulator data set is built according to the feature information. Then the zero-value insulator feature information is used to classify the vector machine, and its parameters are optimized by whale optimization Algorithm. Finally, the actual data is used as test set to test the proposed classification model. By comparing the classification and identification results of PSO-SVM, GS-SVM and WOA-SVM models, the comprehensive evaluation results show that the model obtained by the whale optimization Algorithm optimizing the support vector machine model can identify the zero-valued insulators more accurately and efficiently with a higher accuracy rate.

关键词： Support vector machines Solid modeling Porcelain whales Imaging Insulators whale optimization algorithms Vectors Classification algorithms optimization

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Research on Multi-Transmitter Coil Magnetically Coupled Resonance Wireless Power Transfer System

Research on Multi-Transmitter Coil Magnetically Coupled Reso...

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New Energy and Electrical Technology (ISNEET), International Symposium on

作者： Chuanbo Liu Mengyu Zhang Di Gai Shizhe Ma Yishu Han Guanhua Liu Honghan Qi Jiaolong Lv Xin Ming Yuanlong Bai State Grid Liaoning Electric Power Co. Ltd. Fushun Power Supply Company Fushun Liaoning

ISBN: (数字)9798331505905

ISBN: (纸本)9798331505912

The non-contact power supply approach can avoid many drawbacks of traditional wire-based power supply methods, ensuring the stability of wireless power transfer systems. This paper focuses on the working principle and system structure of magnetically coupled resonance wireless power transfer systems. Based on this, a system with multiple transmitter coils is designed, allowing the system's output power to remain constant under disturbances caused by variations in the coupling coefficient. The mutual inductance model and SS-type compensation structure are selected for design from a circuit perspective. A simulation model for the control design of the wireless power transfer system is built based on simulation software, and the whale optimization algorithm is utilized to tune and optimize the PID control parameters. Analysis of the optimized simulation results indicates that, with PID control, the system can achieve constant output power under variations in mutual inductance, i.e., changes in the coupling coefficient, and exhibits stronger disturbance rejection capabilities.

关键词： Coils Inductance PI control Power supplies Magnetic resonance Wireless power transfer whale optimization algorithms PD control Integrated circuit modeling Power generation

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Comprehensive Survey on Metaheuristic Approaches for Photovoltaic Parameter Identification

Comprehensive Survey on Metaheuristic Approaches for Photovo...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Diaa Salama AbdElminaam Ahmed Taha Asmaa R. Ibrahim Faculty of Computer Science Misr International University Cairo Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Department of Computer Science Obour Higher Institute for Management & Informatics Egypt

ISBN: (数字)9798350367775

ISBN: (纸本)9798350367782

This comprehensive survey delves into the critical task of parameter estimation in photovoltaic (PV) systems, a fundamental challenge in the design and modeling of solar energy solutions. Diode-based models, including the single-diode, double-diode, and three-diode configurations, are widely employed to represent the intricate behavior of PV cells. Accurate parameter identification within these models is essential for precise system performance prediction, component sizing, and economic feasibility assessment. Metaheuristic algorithms, renowned for their effectiveness in tackling complex optimization problems, have emerged as promising tools for PV parameter estimation. This survey meticulously examines a diverse array of metaheuristic algorithms, encompassing genetic algorithms, particle swarm optimization, differential evolution, and contemporary techniques such as grey wolf optimization and whale optimization algorithm. A comparative analysis of these algorithms is conducted, high-lighting their respective strengths, weaknesses, and suitability for PV parameter estimation. Moreover, the survey explores the influence of various factors on the performance of meta-heuristic algorithms in this context. These factors include initialization strategies, parameter tuning, and problem representation. By meticulously investigating these aspects, the paper aims to equip researchers and practitioners with valuable insights for selecting the most appropriate techniques tailored to their specific PV system applications. Through this comprehensive exploration, this survey seeks to contribute to the advancement of PV system design and optimization by providing a clear understanding of the state-of-the-art meta-heuristic algorithms for parameter estimation.

关键词： Surveys Photovoltaic systems Parameter estimation Biological system modeling Computational modeling Photovoltaic cells Metaheuristics Ubiquitous computing whale optimization algorithms Tuning

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Optimizing Feature Selection for IoT Intrusion Detection: A Hybrid Bio-Inspired Method

Optimizing Feature Selection for IoT Intrusion Detection: A ...

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International Conference on Advanced Communication Technologies and Networking (CommNet)

作者： Aouatif Arqane Omar Boutkhoum Abdelmajid El Moutaouakkil Department of Computer Science Chouaib Doukkali University El Jadida Morocco

ISBN: (数字)9798350367027

ISBN: (纸本)9798350367034

With the rapid adoption of Internet of Things (IoT) applications, the risk of cyber-attacks has increased significantly, underscoring the need for efficient intrusion detection systems (IDS). Machine learning models have proven effective for detecting malicious traffic, but the high-dimensional feature space often introduces computational challenges. To address this, we propose a lightweight IDS combining machine learning with a novel twostage hybrid feature selection approach. In the first stage, filterbased methods reduce the feature space to lower computational demands. The second stage applies a hybrid of genetic algorithms and whale optimization to select an optimal subset of features, leveraging the strengths of both algorithms for enhanced search and exploration. For classification, we employ three ensemble learning models, with LightGBM achieving accuracy scores of $\mathbf{9 9. 9 9 \%}$ on the N-BaIoT dataset. Our approach surpasses current methods in performance and classification efficiency.

关键词： Machine learning algorithms Accuracy Biological system modeling Computational modeling Diversity reception Feature extraction whale optimization algorithms Classification algorithms Internet of Things Genetic algorithms

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Improving Load Frequency Control in Power Systems with Energy Storage Devices via FOPID Controllers

Improving Load Frequency Control in Power Systems with Energ...

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Chinese Automation Congress (CAC)

作者： Yanming Li Xi Lu Wenbin Yue Yang Yang China North Vehicle Research Institute Beijing China Chinese Scholartree Ridge State Key Laboratory China North Vehicle Research Institute Beijing China Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Power systems play a crucial role in providing sustainable energy across various sectors, including critical applications such as military equipment. However, optimizing their stability to meet modern energy demands presents a considerable challenge. This paper introduces a novel methodology aimed at improving the stability of a three-zone hydroelectric power system. The approach involves the integration of fractional-order proportional integral derivative (FOPID) controllers and energy storage devices (ESDs), supported by an improved whale optimization algorithm (WOA). The proposed method demon-strates significant efficacy, notably reducing frequency overshoot by 82.2%, from -0.1437 Hz (ISFS) to -0.0256 Hz in Zone 1. This innovative approach offers several advantages, including rapid response times and seamless convergence, thereby leading to improved stability in load frequency control.

关键词： Hydroelectric power generation Power system stability Electrostatic discharges Superluminescent diodes whale optimization algorithms Time factors Time-domain analysis Frequency control Energy storage Convergence

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Short-term Power Load Forecasting Based on WOA-VMD-Attention-LSTM

Short-term Power Load Forecasting Based on WOA-VMD-Attention...

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System Simulation Technology and its Application (CCSSTA), IEEE China Conference on

作者： Siyu Liu Peng Zhang Dahua Li Xiao Yu Qiang Gao He Tian School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China

ISBN: (数字)9798350366600

ISBN: (纸本)9798350366617

In order to improve the accuracy of short-term load forecasting accuracy of power system, a short-term load forecasting method based on the whale optimization algorithm optimized variational mode decomposition (VMD) and long short-term memory network with attention mechanism is proposed. Firstly, the whale optimization algorithm is utilized to optimize the key parameters of VMD, and VMD is used to decompose the original load sequence. Secondly, the Pearson correlation coefficients are employed to select the key influencing factors of load. The selected key factors and each modal component after VMD decomposition form a new data set, and the Attention-LSTM network is used to predict each modal component of VMD decomposition. Finally, the prediction results of each modal component are summed to obtain the final prediction result. Taking the actual load data of a city in Australian as an example, the accuracy and stability of the proposed method are verified by comparing with other models.

关键词： Accuracy Attention mechanisms Load forecasting Urban areas Predictive models Power system stability whale optimization algorithms Stability analysis Long short term memory Load modeling

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