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检索条件"主题词=whale optimization algorithms"

共 377 条记录，以下是91-100 订阅

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Optimal Placement and Sizing of Capacitor Banks in Radial Distribution Systems Using the whale optimization Algorithm

Optimal Placement and Sizing of Capacitor Banks in Radial Di...

引用

IEEE PES Conference on Innovative Smart Grid Technologies - Middle East (ISGT Middle East)

作者： Adham Osama Hatem H. Zeineldin El-Fouly Tarek H.M. Ehab F. El-Saadany EESC Department Advanced Power and Energy Center (APEC) Khalifa University Abu Dhabi UAE

Capacitor banks (CBs) are widely utilized in electrical distribution networks for reactive power compensation. This study introduces a two-step approach for identifying the optimal location and sizing of CBs in a 34-bus radial distribution network. The proper allocation of CBs in distribution networks provides several benefits such as power loss reduction, boosting net savings, and improving feeders’ voltage profiles. The swarm optimization algorithm selected in this study is the whale optimization Algorithm (WOA) to find the optimal sizing and allocation of capacitors in the proposed system. The first step in this study will find the fittest buses for capacitors allocation through the loss sensitivity indices and then the second step comes to optimally identify the best locations and sizes of CBs using the whale optimization algorithm. The optimal capacitor placement (OCP) problem considers minimizing the total cost comprising the active power loss cost, the capacitors purchase, and capacitors installation costs. Three cases are studied and compared, the uncompensated case (without reactive power compensation), the fixed capacitors, and the switched capacitors. The results showed the capability of WOA to improve the overall performance in terms of the annual cost, the percentage of energy loss reduction, and the net saving in ${\$}$/year.

关键词： Reactive power Costs Capacitors Distribution networks Voltage Switches whale optimization algorithms

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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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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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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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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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An Improved whale optimization Algorithm and Its Application on Inverse Scattering Problem

An Improved Whale Optimization Algorithm and Its Application...

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International Symposium on Antennas, Propagation and EM Theory (ISAPE)

作者： Meng Wang Guizhen Lu Communication University of China School of Information and Communication Engineering Beijing China

ISBN: (纸本)9781665418133

whale optimization Algorithm (WOA) is a stochastic optimization algorithm which imitates the predation of whale for searching their preies. Due to its search efficiency, WOA has been broadly utilized to solve sundry kinds of engineering problems. However, the traditional WOA is often trapped into local optima while solving highly ill-posed inverse problems. Therefore, a new improved WOA is proposed by modifying the original searching path. According to the different search mechanisms of the algorithm, the search path is combined and optimized in order to get better constrain for the search direction and enable the search towards the global optimum. One reconstruction of scatterer permittivity test is given to prove the efficiency of improved WOA. As expected, the proposed IWOA achieves better solutions compared with traditional algorithms.

关键词： Microwave technology Inverse problems whales Search problems whale optimization algorithms Permittivity optimization

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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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