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检索条件"主题词=Whale optimization algorithms"

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Research on big data processing framework algorithm optimized by deep learning

Research on big data processing framework algorithm optimize...

引用

Electrical, Automation and Computer Engineering (ICEACE), International Conference on

作者： Shuo Ma Business School Shandong University of Political Science and Law Jinan China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

This paper proposes a deep learning optimization big data processing framework based on Hadoop distributed architecture, Convolutional Neural Network (CNN) and whale optimization Algorithm (WOA). First, the Hadoop distributed file system (HDFS) and MapReduce technologies are used to store and preprocess large-scale data in parallel, and reduce data redundancy and processing delay through efficient data distribution and cleaning. Secondly, as a feature extraction module, the CNN model extracts the spatial features of the data layer by layer by using multi-layer convolution and pooling operations to provide reliable feature representation for subsequent classification and prediction tasks. In order to further improve the performance of the model, this paper introduces whale optimization algorithm (WOA) to optimize the hyperparameters of the CNN model (such as learning rate, convolution kernel size, etc.). By simulating the hunting behavior of whales, WOA automatically adjusts the combination of hyperparameters during model training, thus significantly improving the accuracy and training efficiency of the model. The experimental results show that the proposed Hadoop-CNN-WOA framework has higher accuracy than the traditional single model in processing large-scale image classification tasks.

关键词： Training Deep learning Convolution Computational modeling Distributed databases Big Data Feature extraction whale optimization algorithms Classification algorithms Convolutional neural networks

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WOA based Optimal TIDF Controller for Time Delayed System

WOA based Optimal TIDF Controller for Time Delayed System

引用

IEEE Power India Conference

作者： Vineet Kumar Renu Sharma Department of Electrical and Electronics Engineering MITS Madanapalle Department of Electrical Engineering SOA University Odissa

ISBN: (数字)9798350367065

ISBN: (纸本)9798350367072

This manuscript presents a novel methodology to effectively control time-delayed systems. In this work, authors have proposed a whale optimization Algorithm (WOA) tuned Tilt Integral Derivative Controller with Derivative Filter (TIDF) specifically designed for regulating time-delayed systems characterized by 4th order dynamics. The efficacy of the proposed method has been thoroughly verified by comparing its transient response specifications with those of existing approaches documented in the literature. This comparative analysis demonstrates that the WOA-tuned TIDF controller offers superior performance in terms of transient response, highlighting its suitability for the intended application. Additionally, the robustness of the proposed algorithm has been rigorously tested under conditions involving variable time delays, which are a common and challenging aspect of real-world systems. The results of these tests indicate that the WOA-tuned TIDF controller not only achieves a better transient response but also maintains stability and robustness across different scenarios. Consequently, the proposed methodology provides a reliable and effective solution for controlling time-delayed systems, ensuring both enhanced performance and resilience in various industrial and technological applications.

关键词： Transient response Heuristic algorithms Delay effects Filtering algorithms Control systems whale optimization algorithms Robustness Stability analysis Transient analysis Testing

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Automatic Modulation Recognition Based on Features Fusion of IQH and AAH

Automatic Modulation Recognition Based on Features Fusion of...

引用

IEEE International Conference on Communications in China (ICCC)

作者： Pengwu Wan Xi Hui Dongrui Chen Siying Kang Xu Zhang Xi’an University of Posts & Telecommunications Xi’an China National Key Laboratory of Science and Technology on Space Microwave China Academy of Space Technology Xi’an China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

With the rapid development and popularization of the Internet of Things (IoT), its extensive applications make networks vulnerable to malicious attacks, leading to various security and privacy issues. Automatic modulation recognition (AMR) technology provides crucial support for the stable operation and security of the IoT. Based on this, this paper proposes a modulation recognition method based on histogram feature fusion. Firstly, signal sequences are transformed into asynchronous in-phase quadrature histogram (IQH) and asynchronous amplitude histogram (AAH) to represent signal features. While IQH exhibits a stronger advantage in certain conditions, AAH provides complementary information that can enhance the robustness and accuracy of the recognition process. Therefore, IQH and AAH are fused through discriminant correlation analysis (DCA) to obtain comprehensive feature vectors. Compared with single-input features, the combination of the two inputs will improve the final recognition accuracy. Secondly, the improved whale optimization algorithm (IWOA) is employed to optimize the radial basis function neural network (RBFNN), where the fused feature vector is utilized as input to recognize the modulation type of the input signal. Finally, seven modulation types are identified in simulation experiments. The experimental results indicate that under the additive white Gaussian noise channel model, a signal-to-noise ratio of 6 dB can achieve a recognition accuracy of $9 9 . 5 2 \%$. Compared with other experimental analyses, this method exhibits high performance and robustness.

关键词： Histograms Accuracy Correlation Modulation Robustness whale optimization algorithms Vectors

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Intelligent Baijiu Blending Model Based on Meta-Goal Programming

Intelligent Baijiu Blending Model Based on Meta-Goal Program...

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Asian Control Conference

作者： Ding Wang Zhiyao Zhao Jiabing Yu Xin Zhang School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

This paper proposes a new Baijiu blending optimization method, combining Meta-Goal Programming (MGP) and whale optimization Algorithm (WOA). The proposed method takes into account the complexity of Baijiu's composition and the significant influence of trace components on its flavor profile. To address this, a mathematical modeling framework that surpasses traditional linear and goal programming methods is introduced, which often neglects the nuances of liquor flavor in the pursuit of cost reduction. The study shows that the combination of MGP and WOA improves the accuracy of Baijiu blending by systematically balancing compound concentrations to achieve desired flavor profiles. The experimental results demonstrate the advantages of this approach over traditional methods, partic-ularly in reducing deviations in compound concentrations and better meeting the qualitative goals set by the blenders. This study contributes to the scientific and precise method for Baijiu blending and the intelligent automation of Baijiu production. It lays a theoretical foundation for automated blending.

关键词： Intelligent automation Process control optimization methods Production Programming Mathematical models whale optimization algorithms

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PMSM Inter-Turn Short Circuit Fault Diagnosis Based on WOA-VMD

PMSM Inter-Turn Short Circuit Fault Diagnosis Based on WOA-V...

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Electrical Energy Conversion Systems and Control（IEECSC), IEEE International Conference on

作者： Jinyong Zhang Dingguo Shao Yitong Wei School of Electromechanical Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9798350389357

ISBN: (纸本)9798350389364

The safety and reliability of the motor is the basis and guarantee of the stable operation of the motor, so it is of great significance to be able to quickly and accurately diagnose the early motor faults to avoid unnecessary downtime or even more serious consequences due to motor faults. In this paper, we diagnose the early inter-turn short-circuit fault of the motor from the two aspects of motor fault modeling and early fault signal processing. As the early fault signal of the motor is not obvious, we use the Variable Modal Decomposition (VMD) algorithm to process the fault signal, and use the whale optimization algorithm (WOA) to optimize the parameters of the VMD algorithm. After simulation verification results show that the best combination of parameters obtained after the whale optimization algorithm is applied to the VMD algorithm to decompose the fault signal, which can effectively eliminate the noise interference to extract the fault characteristics.

关键词： Fault diagnosis Torque Velocity control Signal processing algorithms Motors Feature extraction whale optimization algorithms Safety Circuit faults Spectrogram

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Short-Term Load Forecasting Model Based on BWOA-LSSVM

Short-Term Load Forecasting Model Based on BWOA-LSSVM

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Sensors, Electronics and Computer Engineering (ICSECE), IEEE International Conference on

作者： Kun Liu Bingbing Chen Juanjuan Wang Yuxin Yang Guangliang Sun Xiao Guo Weifang Power Supply Company State Grid Shandong Province Power Company Weifang China Key Laboratory of Intelligent Grid Ministry of Education Tianjin University Tianjin China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

In recent years, advancements in new technologies and the emergence of new scenarios have caused a shift from the traditional pure load demand side to a broader load perspective, posing considerable challenges to conventional load forecasting methods. This transition creates an urgent requirement for power systems to develop regional load forecasting techniques tailored to new scenarios by leveraging contemporary theories and technologies. To enhance load forecasting accuracy, this paper introduces an improved whale optimization Algorithm (BWOA) based on the Baleen Search to optimize the Least Squares Support Vector Machine (LSSVM) model. The approach begins by establishing the LSSVM-based forecasting model. Subsequently, the whale optimization Algorithm (WOA) is refined by integrating adaptive weights and the Beetle Antennae Search (BAS) strategy. The BWOA is then employed to optimize the penalty factor of the LSSVM, thereby forming the BWOA-LSSVM model for load forecasting. Simulation comparisons demonstrate that the proposed BWOA-LSSVM model significantly improves the precision of power load predictions.

关键词： Adaptation models Accuracy Load forecasting Computational modeling Predictive models Prediction algorithms whale optimization algorithms Vectors Load modeling Convergence

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Rolling Bearing Fault Diagnosis Based on Improved Multi-Scale Dispersion Entropy and Deep Extreme Learning Machine

Rolling Bearing Fault Diagnosis Based on Improved Multi-Scal...

引用

Automation in Manufacturing, Transportation and Logistics (ICaMaL), International Conference on

作者： Xu Chengjun Shu Sheng Wang Kun School of Transportation and Logistics Engineering Wuhan University of Technology Wuhan China Hainan Institute Wuhan University of Technology Wuhan China

ISBN: (数字)9798350378658

ISBN: (纸本)9798350378665

In response to the challenges associated with the incomplete extraction of fault features in rolling bearings and the resultant low recognition accuracy, this study proposes a fault diagnosis model that integrates local mean decomposition, enhanced multi-scale dispersion entropy, and a whale optimization algorithm to refine the deep extreme learning machine. Initially, the model employs local mean decomposition to break down the original vibration signal into several product function components. Subsequently, spurious product function signal components are eliminated through the application of mutual information and kurtosis screening criteria. The multi-scale fault features of the signal components are then extracted using the improved multi-scale dispersion entropy method. Finally, the extracted fault features are fed into a deep extreme learning machine, which has been optimized via the whale optimization algorithm, for training and testing purposes. Experimental investigations conducted on rolling bearings, alongside comparisons with a control group, demonstrate that the proposed model effectively extracts fault features and enhances the identification accuracy of rolling bearing faults.

关键词： Fault diagnosis Accuracy Extreme learning machines Rolling bearings Feature extraction Entropy whale optimization algorithms Dispersion Monitoring Mutual information

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CleanComm: A Hybrid ACO-WOA-MOA Model for Sustainable Communication

CleanComm: A Hybrid ACO-WOA-MOA Model for Sustainable Commun...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Vaishali Aggarwal Kashvi Goel Ishika Gupta Prerna Ajmani School of Information Technology Vivekananda Institute of Professional Studies-Technical Campus Delhi India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Wireless sensor networks (WSNs) and their applications are on an increasing trend owing to the users’ demands with interest in environmental activities, intelligent cities, and medical assistance. However, because of the inefficiency and limitations of wireless sensor networks, it is still very difficult to optimize the network parameters and performance (energy consumption, area coverage and reliable information transfer). In this paper optimization algorithms including Ant Colony optimization (ACO), Moth Flame optimization (MFO) and whale optimization Algorithm (WOA) to enhance the effectiveness of wireless sensor systems are presented. In this approach, ACO captures and explores functional properties, MFO coordinates the optimization and exploration processes, and WOA concentrates on global exploration. Wide-ranging simulations demonstrate that the recommended approach is more effective than the existing approaches in enhancing energy efficiency, network lifetime and effectiveness of packet delivery. The findings in this paper indicate that the proposed hybrid algorithms support the various requirements of the WSN, thus promoting development of networks that are secure and dependable.

关键词： Wireless sensor networks Energy consumption Smart cities Green communications Power control Interference Routing whale optimization algorithms Reliability optimization

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WOA-VMD and SVM Rolling Bearing Failure Diagnosis

WOA-VMD and SVM Rolling Bearing Failure Diagnosis

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Yawen Zhang Guici Chen Wenbo Wang College of Science Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798350308501

ISBN: (纸本)9798350308518

To address the issue of low accuracy in extracting fault information during rolling bearing fault diagnosis, this study proposes a method using a combination of whale optimization Algorithm-Variational Mode Decomposition (WOA-VMD) and Support Vector Machine (SVM). The proposed method is aimed at improving the accuracy of fault information extraction in rolling bearings. Firstly, the whale optimization algorithm is used to optimize the parameters of variational mode decomposition. Then, the optimized VMD is used to decompose the bearing signal, calculate the steepness and energy entropy of the decomposed modal component, combine the steepness and energy entropy, and construct the feature matrix. Finally, the support vector machine model is used to identify and diagnose bearing faults. Compared with the whale optimization algorithm-empirical mode decomposition model (WOA-EMD), adaptive variational modal decomposition model (AVMD), gray wolf algorithm-variational modal decomposition model (GWO-VMD), the recognition accuracy of the four methods is 83.3333%, 89.7436%,93.5897%and100%, respectively, which is obviously the accuracy of the proposed method is higher.

关键词： Support vector machines Fault diagnosis Adaptation models Rolling bearings Information retrieval whale optimization algorithms Entropy

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Enhancing Fine-Tuning of Pre-trained Language Models for Sentiment Analysis Using Metaheuristic algorithms

Enhancing Fine-Tuning of Pre-trained Language Models for Sen...

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International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Pranav T Pattanashetty Pratyush Pankaj Rishika Kinger Rehan Sayed Arti Arya Richa Sharma Department of CSE PES University Bengaluru India

ISBN: (数字)9798331518127

ISBN: (纸本)9798331518134

Fine-tuning pre-trained language models for specific natural language processing tasks often leads to suboptimal performance due to the vast parameter space and the challenge of finding the most effective model configurations. Traditional fine-tuning methods can be computationally expensive and may not fully exploit the potential of these models, particularly for tasks like sentiment analysis. This work explores the application of metaheuristic techniques, focusing on sentiment analysis using the Stanford Sentiment Treebank 2 (SST2) *** utilize two approaches: Genetic algorithms (GA) and the whale optimization Algorithm (WOA). These methods aim to efficiently search the parameter space and optimize the fine-tuning process of pretrained language models. To enhance efficiency, we incorporate layer freezing techniques along with different crossover strategies. In the results obtained, GA with Simulated Binary Crossover (SBX) and 30% layer freezing achieved 94.02% accuracy, while WOA with SBX crossover and 30% layer freezing achieved 94.67% accuracy. These outcomes highlight the potential of integrating evolutionary algorithms, nature-inspired optimization techniques, and strategic layer freezing with deep learning finetuning processes for NLP tasks.

关键词： Adaptation models Sentiment analysis Analytical models Accuracy Heuristic algorithms Computational modeling Metaheuristics whale optimization algorithms Tuning Genetic algorithms

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