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检索条件"主题词=whale optimization algorithms"

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Optimized PV Panel Control in Hybrid DC Microgrid Systems Using whale optimization & Reinforcement Learning

Optimized PV Panel Control in Hybrid DC Microgrid Systems Us...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： Kathir D Umesh Prabhu H Maathavan R Department of EEE St. Joseph’s College of Engineering OMR Chennai

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

As the demand for reliable and sustainable energy grows, DC microgrids powered by renewable sources are becoming an efficient alternative for modern power systems. This research introduces an optimized control strategy for a DC microgrid that combines solar, wind, and batteries, ensuring efficient power management and uninterrupted energy supply to connected loads. The system leverages the whale optimization Algorithm combined with Reinforcement Learning to enhance Maximum Power Point Tracking for solar panels, enabling real-time adaptation to changing environmental conditions. A dedicated power converter controls the DC bus voltage, and the battery circuit maximizes charging and discharging to maintain system stability. Additionally, a power flow management algorithm is developed to ensure optimal energy distribution among the three sources. The proposed approach is implemented and tested in SIMULINK under varying solar and wind conditions. Simulation results demonstrate improved energy harvesting, faster convergence, enhanced voltage regulation, and higher system efficiency. This research contributes to the advancement of smart and self-sustaining DC microgrids, providing an intelligent solution for renewable energy management.

关键词： Maximum power point trackers Renewable energy sources Lighting Microgrids Reinforcement learning Power system stability whale optimization algorithms Stability analysis Batteries Voltage control

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Decision Tree with whale optimization Algorithm (DT-WOA) for QoS-Based IoT Service Composition

Decision Tree with Whale Optimization Algorithm (DT-WOA) for...

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Electrical, Computer, Telecommunication and Energy Technologies (ECTE-Tech), International Conference on

作者： Souhila Ait Hacene Ouhadda Samia Chibani Sadouki Abdelkamel Tari LIMED laboratory Faculty of Exact Sciences University of Bejaia Bejaia Algeria LIMED laboratory Faculty of Exact Sciences Laboratoire LITAN Higher School of Sciences and Technologies of Computing and Digital (ESTIN) Bejaia Algeria

ISBN: (数字)9798350388480

ISBN: (纸本)9798350388497

From smart homes to smart cities, the Internet of Things (IoT) has revolutionized our lives by connecting billions of devices into an interconnected network. Each smart device offers specific functions, such as remote door unlocking or sending alerts, which we call IoT services. To meet the complex needs of users, combining multiple atomic IoT services is required, as a single service is often insufficient. The increasing number of IoT services offering similar functionality makes service composition based on Quality of Service (QoS) attributes more challenging. To address this issue, we propose a DT-WOA approach combining a Decision Tree (DT) with the whale optimization Algorithm (WOA). We use DT-WOA to find a close-to-optimal solution that meets the QoS user requirements. Simulation results indicate that DT-WOA outperforms the original WOA in terms of fitness value by effectively reducing the search space.

关键词： Training Machine learning algorithms Simulation Quality of service Smart homes whale optimization algorithms Classification algorithms Internet of Things Decision trees Smart devices

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Diagnosing Diabetes Using Binary whale optimization Algorithm-Based Feature Selection

Diagnosing Diabetes Using Binary Whale Optimization Algorith...

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Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Marium Khairalla Ali Ahmed Lawgali Computer Science (Facutly of Information Technology) University of Benghazi Benghazi Libya

ISBN: (数字)9798350372632

ISBN: (纸本)9798350372649

Early diagnosis of diabetes is critical for prompt treatment that can prevent the disease's progression to potentially lethal complications. Due to factors such as high blood pressure, genetic diabetes, and modern lifestyles that include inactivity, diabetes is one of the most common and severe diseases in the world. Using machine learning to assist in the creation of quick and precise diagnoses is one endeavor being made in the health sector. Feature selection approaches are a crucial step towards achieving high classification accuracy since they improve the efficiency of learning algorithms. In order to detect diabetes disease, the features from the Pima Indians Diabetes (PID) dataset were selected using a binary whale optimization algorithm (BWOA). After that, the classification is applied by the Gaussian process classifier (GPC) and linear discriminant analysis (LDA) independently. The proposed BWOA-GPC system obtained the highest classification accuracy of up to 85.94%.

关键词： Training Accuracy Machine learning algorithms Standardization Feature extraction whale optimization algorithms Diabetes

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Optimal Configuration of Grid-Forming Energy Storage System Based on the whale optimization Algorithm

Optimal Configuration of Grid-Forming Energy Storage System ...

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Energy, Power and Electrical Technology (ICEPET), International Conference on

作者： Xin Ma Wenbo Gu Sizhuo Lv Chao Zheng Electric Power Research Institute of State Grid Ningxia Electric Power Co. Ltd. Yinchuan Chian China Electric Power Research Institute Beijing China

ISBN: (数字)9798350352658

ISBN: (纸本)9798350352665

This paper investigates the optimal configuration of grid-forming energy storage systems (GFM-ESS) in a power grid with a high proportion of renewable energy using the whale optimization Algorithm (WOA). The model aims to minimize the GFM-ESS capacity while ensuring that transient overvoltage remain within safe limits in wind farms. The optimization model incorporates penalties for transient overvoltage exceeding a specific threshold. The algorithm iteratively determines the best capacity and location of GFM-ESS to stabilize the system and maintain secure voltage levels. The simulation results confirm that strategic GFM-ESS deployment can significantly improve the stability and resilience of power grids integrated with high shares of renewable energy.

关键词： Renewable energy sources Power system stability Wind farms whale optimization algorithms Stability analysis Power grids Voltage control

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Adaptive Random Evolution whale optimization Algorithm (AREWOA) Based Workflow Scheduling in a Virtualized Cloud Environment

Adaptive Random Evolution Whale Optimization Algorithm (AREW...

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International Conference on Computing and Networking Technology (ICCNT)

作者： R. Nathiya S.K. Piramu Preethika Department of Information Technology School of Computing Sciences VEL’S Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Cloud workflow task scheduling refers to assigning workflow tasks submitted by users to appropriate computer resources for execution. The workflow scheduler assigns the user request to the available Virtual Machine (VM) hosted on cloud servers during scheduling. However, while limiting energy consumption, the workflow application processing schedule length (makespan) and associated costs must stay within the users budget and deadline. The tasks are chosen using the optimal scheduling technique to reduce Energy Consumption (EC) and maximize Resource Utilization (RU) without compromising system performance. In this paper, Adaptive Random Evolution whale optimization Algorithm (AREWOA) based scheduling is proposed to reduce EC, maximize RU, and workflow execution time. AREWOA is performed based on the dual-operation strategy partnership. Initially, the Gaussian distribution is used in the randomization procedure to raise the variety of the population. Secondly, a new skip step factor is introduced to enhance the optimizer capability to solve local optimum, and specific reinforcement is applied to the hunter process to enhance the whale’s exclusive exploration or exploitation skills. An adaptive weight factor is applied to maintain algorithm stability and a balance among exploration and exploitation. Dynamic provisioning of infinite heterogeneous resources, AREWOA is thought to demonstrate cloud-based Infrastructure-as-a-Service (IaaS). This approach is simulated in Cloud Simulator and the results shows that the efficiency of the methods.

关键词： Cloud computing Energy consumption Schedules Processor scheduling Heuristic algorithms System performance Optimal scheduling whale optimization algorithms Stability analysis Servers

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whale optimization Algorithm (WOA) for Task Scheduling Problem in Multi-Processors Environment

Whale Optimization Algorithm (WOA) for Task Scheduling Probl...

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IEEE Symposium on Industrial Electronics & Applications (ISIEA)

作者： Saiful Izwan Suliman Mohammad Nur Khalish Yuslinda Wati Mohamad Yusof Farah Yasmin Abdul Rahman School of Electrical Engineering College of Engineering Universiti Teknologi MARA (UiTM) Shah Alam Selangor

ISBN: (数字)9798350386868

ISBN: (纸本)9798350386875

Use of multiprocessors applications to solve complex problems in computer's system contributes to the increase in performance and speed of today's computer. In multiprocessors environment, it is very important to have a great strategy or method on scheduling the processors that are utilized to handle a specific task in the computer system. As each scheduling problem involves problem-specific variables, multiprocessors need different and appropriate algorithm for their problems. This study introduces the whale optimization Algorithm (WOA) for task scheduling. Comparisons with other existing methods were also conducted to highlight the efficiency of the proposed WOA in performing task scheduling.

关键词： Program processors Processor scheduling Instruments Scheduling whale optimization algorithms Computational efficiency Resource management

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A whale optimization Algorithm Based on Simulated Annealing and Adaptive Mutation

A Whale Optimization Algorithm Based on Simulated Annealing ...

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Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Yumei Zhu Yushu Lai Chuo Mei Tianguo Li School of Electronic and Information Engineering Chongqing Three Gorges University Chongqing China School of Mechanical Engineering Chongqing Three Gorges University Chongqing China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

Aiming at the problems of the whale optimization algorithm, such as slow convergence speed and easy to fall into local optimization, the algorithm is proposed to be improved by integrating simulated annealing and adaptive mutation strategy. Chaotic inverse learning mechanism is used to initialize the population, and nonlinear convergence factor and dynamic inertia weights are introduced to timely improve the population's global optimization ability in the early stage and local accurate optimization ability in the late stage. Simulation experiments are carried out on 18 basic test functions and verified by statistical methods, comparing the Gray Wolf algorithm, whale optimization algorithm, Particle Swarm algorithm and Genetic algorithm, and the experimental results show that the improved whale optimization algorithm has a better convergence speed and stability.

关键词： Learning systems Statistical analysis Heuristic algorithms Perturbation methods Sociology Simulated annealing whale optimization algorithms

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Fracture Detection in X-Ray Images Using whale optimization Algorithm-Enhanced Bi-Directional LSTM Networks

Fracture Detection in X-Ray Images Using Whale Optimization ...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Raj Gaurang Tiwari Mahesh Manchanda Ambuj Kumar Agarwal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Greater Noida India

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

Rapid and reliable fracture detection in X-ray images is essential for medical diagnosis. This research offers a hybrid deep learning strategy that leverages the whale optimization Algorithm (WOA) to choose the most significant features from AlexNet and ResNet50. WOA enhances feature selection by searching for relevant attributes. To identify fractures, a Bi-directional Long Short-Term Memory (Bi-LSTM) network is employed. The proposed hybrid fracture diagnosis method beats existing methods with 96.86% accuracy. Integration of optimization approaches with powerful deep-learning architectures may improve medical imaging diagnosis accuracy.

关键词： Deep learning Visualization Accuracy Soft sensors Feature extraction whale optimization algorithms optimization X-ray imaging Standards Residual neural networks

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ADM1-Guided optimization of anaerobic digestion Model Parameters via whale optimization Algorithm

ADM1-Guided Optimization of anaerobic digestion Model Parame...

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Advances in Electrical and Communication Technologies (ICAECOT), International Conference on

作者： Messili Oualid Chaabna Abdelhani Semcheddine Samia Power Electronic and Industrial Control Laboratory (LEPCI) UFAS Setif1 Setif Algeria

ISBN: (数字)9798350353754

ISBN: (纸本)9798350353761

Anaerobic Digestion (AD) technology, known for its waste recycling and environmental clean-up capabilities, also generates renewable energy in the form of biogas. The need to reduce dependence on fossil fuels has extensively stimulated interest in this process. The process is known for its complexity, high sensitivity, and potential for instability, which is inherently reflected in the models describing it. Models contain uncertain parameters and highly sensitive to process noise. Parameter identification plays a crucial role during modeling and is a delicate task preceding control law development. It is mandatory to have the best possible estimates of a model that guarantees efficient predictions. This paper focuses on the optimization of stoichiometric and kinetic coefficients of the Acidogenesis Methanogenesis 2 model (AM2). We present in this study the use of the whale optimization Algorithm (WOA) to optimize AM2 model parameters and validate the results using data acquired from a Anaerobic Digestion Model $\mathbf{N}^{\circ} 01$ (ADM1). The identification algorithm was implemented in MATLAB 2018. To validate the effectiveness of our proposed approach, we conduct a comparative analysis with Genetic Algorithm (GA). The results, despite certain limitations, underscore the robustness of WOA for parameter estimation.

关键词： Renewable energy sources Parameter estimation Sensitivity Biological system modeling whale optimization algorithms Data models Robustness Recycling optimization Genetic algorithms

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SURAKSIT: A Secure Resource Allocation Scheme Using whale optimization for D2D Communication

SURAKSIT: A Secure Resource Allocation Scheme Using Whale Op...

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IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： Nilesh Kumar Jadav Sudeep Tanwar Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

As the demand for mobile data traffic continues to rise, short-range communication, such as device-to-device (D2D) communication, will become a paramount research focus. D2D communication is severely strained by distinct challenges, such as interference, resource allocation, and security, that degrade its performance in terms of sum rate, resource allocation fairness, and system throughput. In this article, we aim to maximize the overall system throughput of the D2D communication by employing a bio-inspired algorithm - whale optimization algorithm (WOA). It uses whales’ social behaviour to detect the optimal D2D users based on their signal-to-noise-plus-interference ratio (SINR) values. Furthermore, we employed an isolation forest algorithm with the intent of identifying potential eavesdroppers who illicitly augment their resources (transmission power) to intercept ongoing D2D communication. The isolation forest algorithm offers anomaly scores that identify deviations in their resources, signalling the presence of entities that may be eavesdropping. The WOA-based access control restricts the eavesdropper and allows only the optimal D2D users to participate in the resource allocation process, thus improving the throughput of the D2D communication. Lastly, the proposed scheme is assessed using different assessment metrics, such as fairness (0.861), system throughput (28.67 Mbps), and convergence rate.

关键词： Access control Performance evaluation Process control Interference Forestry Throughput whale optimization algorithms

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