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检索条件"主题词=Whale Optimization Algorithms"

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Delay-Aware Probabilistic Cache Placement for Scalable Videos in Satellite-Terrestrial Networks

Delay-Aware Probabilistic Cache Placement for Scalable Video...

引用

International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Jiayi We Tianheng Xu Yuling Ouyang Jinfeng Tian Ting Zhou School of Microelectronics Shanghai University Shanghai China Chinese Academy of Sciences Shanghai Advanced Research Institute Shanghai China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

With the rapid expansion of video services, caching has emerged as a promising solution to reduce retrieval delay and alleviate backhaul pressure. Specifically, caching video content on user devices and satellites is particularly effective for users without ground base station services. However, addressing the challenge of meeting users' varying video quality demands while reducing content transmission delay is critical. Therefore, this paper investigates a layered caching scheme based on Scalable Video Coding, which encodes videos into different layers to provide various quality levels and caches video content on different nodes. Furthermore, an optimization problem for the probabilistic cache placement scheme in a multi-layer caching model is proposed and solved using the Quasi-Oppositional whale optimization Algorithm. The simulation results indicate that, compared to other algorithms and benchmark schemes, the proposed scheme effectively reduces transmission delay.

关键词： Wireless communication Satellites Simulation Signal processing algorithms Static VAr compensators Probabilistic logic Space-air-ground integrated networks whale optimization algorithms Delays Video recording

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Research on Teaching Quality Evaluation Using Heuristic-Based Apriori Algorithm with Data Mining

Research on Teaching Quality Evaluation Using Heuristic-Base...

引用

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

作者： Fei Zhao Fan Yang Harbin Cambridge University Heilongjiang Harbin China

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

An evaluation of teaching quality is a decision making of teaching value of teachers and learning of the students, becomes the significant role of teaching management and procedures in academics. Nevertheless, the operation of the executing the teaching quality evaluation is relatively unwieldy, frequently needed the accomplishment of the maximum number of data evaluation tasks. To overcome this problem, this research aims to proposes Heuristic approach for the effective teaching quality evaluation approach in the academics. Hence, this research proposes combined Firefly with whale optimization Algorithm based Apriori Algorithm (FWOA-AA) for the evaluation of the teaching quality. The data mining approach for the manual teaching is utilized and the FWOA-AA approach is utilized for attain the effective classification of the numerous students test score. The classification approach effectively chooses the classification features according to the rate of information gain. introduced FWOA-AA approach attains the effective classification accuracy of 95.01%, 97.39 % , 94.93%, 98.40% and 97.98% on the different data sizes like 100MB, 200 MB, 300 MB, 400 MB and 500 MB respectively.

关键词： Deep learning Accuracy Heuristic algorithms Education Focusing Manuals Network security whale optimization algorithms Classification algorithms Data mining

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Distributed Task Offloading in Mobile Edge Computing using Metaheuristics

Distributed Task Offloading in Mobile Edge Computing using M...

引用

International Conference on Communications, Signal Processing, and their Applications (ICCSPA)

作者： Ala’ A. Samarneh Abdallah Y. Alma'aitah Dept. of Network Engineering and Security Jordan University of Science and Technology Irbid Jordan

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

Mobile Edge Computing (MEC) is emerging as a prominent technology that enables different sophisticated mobile applications. With the high transmission rates provided by 5G and beyond and the MEC technology, satisfying applications' strict delay requirements is possible. By providing high computation power closer to the users at the network edge, various applications such as smart cities, context awareness, and content distribution can provide users with a better Quality of Experience (QoE) using MEC. However, offloading all tasks to the MEC is inefficient, as transmitting tasks to the MEC drains the UE battery and might create congestion at the network edge. Therefore, the UE must determine the subset of tasks to offload to the MEC to minimize energy consumption and execution delays. In this work, we investigate a UE-driven offloading approach utilizing three metaheuristic algorithms: Hybrid Improved whale optimization Algorithm (HI-WOA), Tug of War optimization (TWO), and Adaptive Equilibrium optimization (AEO), and priority queues. We formulate the offloading decision as a constrained multi-objective optimization problem, where the objectives are to minimize the energy consumption and execution delay at the UEs. The HI-WOA, AEO, and TWO demonstrated robust performance through extensive simulations with an average Qualified Tasks Ratio (QTR) of 99.75%, 99%, and 94% respectively, and a significant reduction in the execution delays and energy consumptions under different workloads with varying requirements.

关键词： Energy consumption Multi-access edge computing 5G mobile communication Smart cities Metaheuristics Signal processing algorithms Signal processing whale optimization algorithms Delays Quality of experience

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Energy-Efficient Prediction of Hourly Energy Consumption in Fog/Edge Computing Using Optimized Artificial Neural Networks

Energy-Efficient Prediction of Hourly Energy Consumption in ...

引用

Emerging Technologies (GCET), Global Congress on

作者： Hussam N. Fakhouri Mohannad Alkhalaileh Ahmad Hwaitat Eman Zaitoun Data Science and Artificial Intelligence Department College of Education Humanities and Social Sciences Al Ain University Al Ain United Arab Emirates

ISBN: (数字)9798331542603

ISBN: (纸本)9798331542610

The rapid increase in global energy demand necessitates innovative strategies for efficient energy management, particularly in decentralized computing infrastructures such as fog and edge computing. These technologies reduce latency and bandwidth usage by bringing computation closer to the data source, but they introduce new energy management challenges. Accurate prediction of hourly energy consumption is crucial for optimizing these systems’ operation and sustainability. This study proposes a novel approach for predicting hourly energy consumption using a feed-forward multi-layer perceptron (MLP) artificial neural network (ANN) optimized by state-of-the-art metaheuristic algorithms. Historical energy consumption data, including computational workloads and environmental factors, train the ANN. Five metaheuristic algorithms—Marine Predator Algorithm (MPA), Four Vector Intelligent Metaheuristic (FVIM), whale optimization Algorithm (WOA), Equilibrium Optimizer (EO), and Self-Adaptive Differential Evolution (SaDE)—optimize the ANN’s weights. Results show that the Equilibrium Optimizer (EO) significantly enhances predictive accuracy, outperforming other algorithms. This optimized ANN model improves resource allocation, load balancing, and energy cost savings, contributing to the sustainability of fog and edge computing infrastructures. The findings highlight the importance of advanced predictive analytics in managing energy consumption within decentralized computing environments.

关键词： Energy consumption Accuracy Metaheuristics Artificial neural networks Prediction algorithms Energy efficiency whale optimization algorithms Vectors Sustainable development Edge computing

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Cubic Chaotic Initialization Joint Orthogonal Crossbar Strategy to Improve the whale Algorithm

Cubic Chaotic Initialization Joint Orthogonal Crossbar Strat...

引用

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

作者： Ying Xia Qingsong Huo Yanchun Wang Yinbei He School of Communication and Electronic Engineering Qiqihar University Qiqihar China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

After the invention and popularization of computer and the continuous innovation and development of predecessors, swarm intelligence optimization algorithm theory has become an important tool in many fields. For example, whale algorithm is widely used in circuit automatic design, automatic control system and optimal design problems in aeronautical engineering. However, whale algorithm has some problems: the local search ability is weak, it is easy to fall into the local optimal solution, and the convergence speed is slow. This paper proposes an improved whale algorithm (COOLWOA): cubic chaotic mapping is used instead of random method to improve population diversity, and orthogonal opposition learning strategy is used to generate the design matrix of new solutions to ensure good coverage and diversity of new solutions in the whole search space. Then, the local optimization efficiency and global optimization performance of whales are enhanced by using vertical and horizontal strategy to improve the global search and utilization ability. Through MATLAB experiments, the results show that compared with the basic whale optimization algorithm, the proposed algorithm can improve the convergence speed and search ability.

关键词： Technological innovation Heuristic algorithms whales Space cooling Benchmark testing whale optimization algorithms Statistics

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Fault Diagnosis of Rolling Bearing Based on WOA-VMD-LSTM

Fault Diagnosis of Rolling Bearing Based on WOA-VMD-LSTM

引用

Intelligent Control, Measurement and Signal Processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Song Xue Huazhan Gui Xinyu Qin Kang Xia Yihui Tao Jiaoyang Xi Northern Information Control Research Institute Group Co. Ltd Nanjing China School of Instrumentation Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

In order to solve the problems of difficulty in extracting fault features of rolling bearings and the impact of external excitation on the accuracy of fault diagnosis, this paper proposes a fault diagnosis method based on whale optimization algorithm and LSTM(Long Short Term Memory) improved variational mode decomposition combined with support vector machine optimized convolutional neural network. Firstly, the variational mode decomposition of the vibration signal of the rolling bearing is carried out, and the minimum envelope entropy is taken as the fitness function, and the whale optimization algorithm is used to determine the number of decomposition modes and the secondary penalty factor of variational mode decomposition. Secondly, the LSTM noise reduction network is constructed, and the modal components decomposed by the variational mode are introduced into the noise reduction network, and the vibration signal after noise reduction is obtained by reconstructing each mode after noise reduction. Finally, support vector machine is used as the classifier of the convolutional neural network, and the vibration signal after noise reduction is input into the convolutional neural network to realize fault diagnosis. The analysis of experimental results shows that the method proposed in this paper has high fault diagnosis accuracy and better robustness.

关键词： Fault diagnosis Vibrations Support vector machines Accuracy Noise reduction Rolling bearings Signal processing algorithms Feature extraction whale optimization algorithms Convolutional neural networks

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Credit Rating Prediction Based on WOA-Cross-Validation-LightGBM

Credit Rating Prediction Based on WOA-Cross-Validation-Light...

引用

International Conference on Communications, Information System and Computer Engineering (CISCE)

作者： Zichang Sun Shandong University Weihai China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

One of the key challenges faced by banks in the context of continuous growth in customer loan demand is how to effectively execute customer credit ratings. This study introduces SMOTE to solve the problem of data imbalance and utilizes RFE_XG-Boost for feature importance assessment to screen out the key features that have a significant impact on credit ratings. Based on these selected features, the LightGBM model is used to predict customer credit ratings. To further optimize the model performance, this paper applies the whale optimization Algorithm (WOA) to adjust the parameters of the LightGBM model and combines with the 5-fold cross-validation technique to enhance the model prediction accuracy. Using this method, the study significantly improves the accuracy of the model prediction. The experimental results confirm that the LightGBM model, which combines SMOTE, RFE_XGBoost feature selection and WOA, shows high accuracy and practicality in credit rating prediction. The methodology of this study provides an efficient solution for bank credit rating and lays the foundation for future research in similar areas.

关键词： Radio frequency Technological innovation Accuracy Predictive models Prediction algorithms Feature extraction whale optimization algorithms

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Intelligent and Optimized Deep Learning Techniques for Analyzing the IoMT-Based Remote Patient Monitoring Systems

Intelligent and Optimized Deep Learning Techniques for Analy...

引用

Emerging Research in Computational Science (ICERCS), International Conference on

作者： Sayyed Johar G R Manjula Dept of AIML JNNCE-Shivamogga Visveswaraya Technological University Belagavi India Dept of Data Science JNNCE-Shivamogga Visveswaraya Technological University Belagavi

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

The IoMT has dramatically empowered the process of remote patient monitoring (RPM) through much better real-time data collection and health analysis. The real struggle lies in categorizing and evaluating the patient’s health-related datas due to its complexity and unbalanced nature. This study proposes an Intelligent W-DQN model based on a whale-optimized Deep-Q-Network (W-DQN) to identify and categorize pathological information, an LSTM-QDA model for recognizing temporal patterns and reducing dimensions, and a PathoDimClassifier for selecting features to address imbalanced data and noise issues effectively. optimization techniques, including the whale optimization Algorithm and the Adam optimizer, are applied to improve accuracy rates while decreasing training duration and minimizing errors. The approach seeks to enhance health monitoring in time by providing accurate categorization and prompt medical interventions to ultimately enhance patient care results.

关键词： Training Patient monitoring Pathology Accuracy Scientific computing Noise whale optimization algorithms Data models Real-time systems Pattern recognition

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Considering the Green Environmental Value Attribute of Electricity: A Deep Learning Model for Green Electricity Price Forecasting

Considering the Green Environmental Value Attribute of Elect...

引用

IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Ran Ding Haixiang Xu Yiming Yao Wenyu Zhang Bin Wang Xiaochuan Xu Hongyong Liu State Grid Jibei Electric Power Co. Ltd Beijing China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

Against the backdrop of the "dual carbon" goals, the green electricity trading market is gradually developing. Currently, the formation mechanism of green electricity prices is complex, environmental premiums are not unified, and the impact paths are not clear, making the prediction of green electricity prices extremely challenging. Therefore, this paper proposes a deep learning model for forecasting green electricity prices that takes into account the green environmental value attributes of electricity. Firstly, the mechanism of green electricity price formation is studied to explore the impact paths of its influencing factors; then, a quantification index system for green electricity environmental value is constructed; subsequently, the LSTM neural network parameters are optimized using the whale optimization Algorithm (WOA) to build the forecasting model; finally, a simulation experiment is conducted in a green electricity market in North China, and the results show that the proposed model has a prediction accuracy of 91.7%, achieving accurate forecasting of green electricity prices, which can provide reference for market trading entities to formulate strategies.

关键词： Deep learning Accuracy Electricity Green products Predictive models Prediction algorithms whale optimization algorithms Power markets Forecasting Long short term memory

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An Interoperability Test Case Generation Method for State Transition and Parameter Combination Coverage

An Interoperability Test Case Generation Method for State Tr...

引用

Frontiers Technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Changan Wei Ji Xu Zhong Bo Yunlong Sheng Feixuan Wang School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

This paper addresses the critical role of interoperability testing for launch and control electronic information systems and introduces a novel test case generation method leveraging the whale optimization Algorithm (WOA). With the increasing complexity of launch missions, ensuring seamless coordination between these systems is of paramount importance. By constructing state transition models and optimizing parameter combinations, WOA can efficiently generate high-coverage test cases that validate system interoperability. Experimental results demonstrate that WOA significantly outperforms traditional algorithms, such as Particle Swarm optimization (PSO) and Genetic Algorithm (GA), in terms of the number of test cases generated, convergence speed, coverage rate, and fitness value. This method presents a robust solution for enhancing the testing efficiency and reliability of launch and control electronic information systems in the context of complex mission scenarios.

关键词： Reliability theory Control systems Solids whale optimization algorithms Particle swarm optimization Interoperability Testing Information systems Genetic algorithms Convergence

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