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检索条件"主题词=Whale Optimization Algorithms"

共 403 条记录，以下是101-110 订阅

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whale optimization Based MPPT for Partial Shaded PV Powered Water Pumping System Driven by SRM

Whale Optimization Based MPPT for Partial Shaded PV Powered ...

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Sustainable Power & Energy (ICSPE), International Conference on

作者： Sunil C. Kalshetti Rabah Ismail Hashem Al-Mattarneh G. Divya Ramu Bhukya R. Ahila N. Srinivas Department of Electronics Engineering Walchand Institute of Technology Solapur Maharashtra India Civil Engineering Department Jadara University Irbid Jordan Department of Civil Engineering Yarmouk University Irbid Jordan Department of EEE CVR College of Engineering Hyderabad India EEE Shri Vishnu Engineering College for Women (A) West Godavari Andhra Pradesh India Department of CSE New Prince Shri Bhavani College of Engineering and Technology Chennai Tamil Nadu India Dept. of EEE Vardhaman College of Engineering Hyderabad Telangana India

ISBN: (数字)9798350366051

ISBN: (纸本)9798350366068

Water Pumping Systems (WPSs) play an essential role in meeting the daily needs of society; however, they often demand a significant amount of energy, which can exert pressure on the main electrical grid. Establishing autonomous Photovoltaic (PV) powered WPSs in both rural and urban regions is recommended to alleviate the pressure on the utility grid. WPSs are frequently utilized for agricultural, potable, and industrial applications. The switched reluctance motor (SRM) is attracting interest from researchers because of its many benefits, among the different types of motors on the market. This study examines the performance of an 8/6 pole SRM in order to optimize torque output for the purpose of lifting water from significant depths below the surface. Nevertheless, the addition of batteries to WPSs may lead to a rise in the total expenses and necessitate regular upkeep, such as replacing batteries more often because of their shorter lifespan in comparison to PV modules. Hence, this document suggests the integration of a photovoltaic-powered switched reluctance motor-operated wireless power transfer system, employing a sliding mode control for a sensorless speed based direct torque control. Furthermore, the integration of a whale optimization Algorithm (WOA) with the Perturbed & Observed (P&O) mechanism aims to enhance energy harvesting from PV cells, particularly in scenarios of partial shading. The hybrid WOA-P&O approach has been seamlessly incorporated with the converter controller in order to remove the necessity of an additional DC circuit for the utilization of the maximum power point tracker. The article conducts thorough OPAL RT based Hardware- in the – Loop to verify the effectiveness of the proposed approach across different scenarios.

关键词： Maximum power point trackers Photovoltaic systems Torque Torque control Wireless power transfer Switches Switched reluctance motors whale optimization algorithms Batteries Sliding mode control

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Hybrid energy storage method based on parameter optimization VMD to stabilize output power fluctuation of photovoltaic power plant

Hybrid energy storage method based on parameter optimization...

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

作者： Anyu Wang Yuzi Chen Yuxiang Wang Yuan Yue School of Electrical and Power Engineering Hohai University Nanjing China

ISBN: (数字)9798350356472

ISBN: (纸本)9798350356489

Driven by China’s carbon reduction policies, photovoltaic (PV) power generation has rapidly developed, with installed capacity reaching 536 GW. The significant fluctuations in PV output caused by the variability in solar radiation, coupled with the limited ability to store generated electricity, restrict its practical application. This study addresses these challenges by proposing an optimized Variational Mode Decomposition (VMD) enhanced with a Niching-based Hybrid whale optimization Algorithm (NH-WOA). The decomposition results are used to allocate power within a hybrid energy storage system (HESS) comprising hydrogen storage and lithium batteries. The power allocation strategy is designed based on the specific characteristics of these storage components. Validation using data from a 7.5 MW PV plant in Jiangsu demonstrates that this method effectively reduces PV power fluctuations, ensuring compliance with grid stability requirements and enhancing the overall efficiency and reliability of the PV power system.

关键词： Photovoltaic systems Fluctuations Smoothing methods Power system stability Hybrid power systems whale optimization algorithms Solar radiation Standards Signal resolution Energy storage

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Exploring the Potential of Hybrid whale optimization Algorithm: A Literature Review

Exploring the Potential of Hybrid Whale Optimization Algorit...

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Information Technology International Seminar (ITIS)

作者： Henni Endah Wahanani Andreas Nugroho Sihananto Naufal Tafindya Putra Maulana Aditya Furqon Aryanda Bevantyo Satria Pinandhita Shahzada Sabih Anandian Hartanto Department of Informatics Universitas Pembangunan Nasional Veteran Jawa Timur Surabaya Indonesia

ISBN: (数字)9798331521295

ISBN: (纸本)9798331521301

The whale optimization Algorithm (WOA), inspired by humpback whales' hunting behavior, has emerged as a promising metaheuristic optimization technique. This literature review delves into the algorithm's core principles, exploring its applications across diverse domains and assessing its performance in comparison to other optimization methods. By systematically examining existing research, this study aims to identify the strengths, weaknesses, and potential improvements of WOA. Furthermore, it seeks to uncover emerging trends and research gaps, providing valuable insights for researchers and practitioners seeking the opportunity to implement WOA for solving complex optimization problems. This comprehensive analysis will contribute to the advancement of WOA as a robust and versatile optimization tool in multi-dimensional problems. Moreover, the review identifies key strengths of WOA, such as its simplicity and ease of implementation, while also addressing its limitations, including sensitivity to parameter settings and potential stagnation in local optima. The analysis further uncovers emerging trends in WOA research, such as hybridization with other algorithms and adaptations for specific problem domains. By pinpointing these research gaps, this review aims to guide future investigations and practical implementations of WOA, ultimately enhancing its efficacy as a powerful optimization tool for tackling complex, multi-dimensional problems across diverse applications.

关键词： Seminars Sensitivity Manufacturing processes Metaheuristics optimization methods Market research whale optimization algorithms Robustness Tuning Systematic literature review

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Enhanced Arabic Feature Selection using an Improved whale optimization Algorithm

Enhanced Arabic Feature Selection using an Improved Whale Op...

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Information Technology (ACIT)

作者： Meryeme Hadni Mounir Gouiouez Hassane Hjiaj Meryeme Amane FSJESK Cadi Ayyad University Marrakech Morocoo PSCS Laboratory SIDI Mohamed Ben Abdellah University Fez Morocco Department of Mathematics Faculty of Science Abdelmalek Essaadi University ADSES Laboratory Sidi Mohammed Ben Abdellah University Fez Morocco

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Research in the field of Natural Language Processing for Arabic documents that incorporate metaheuristic techniques remain limited, primarily due to the intricate nature of the language, which encompasses challenges related to its structure, morphology, syntactic analysis, grammatical conventions, and derivational processes. This study introduces a novel approach for the processing of Arabic documents, which aims to enhance the whale optimization algorithm. This approach functions as a supervised learning technique, emulating the behaviors of whales, including bubble netting and spiral movements toward their targets. The proposed methodology represents an advancement of the traditional whale optimization algorithm, characterized by its adaptability and efficiency. It effectively integrates exploration and exploitation strategies, drawing inspiration from the natural behaviours of humpback whales while also incorporating the beneficial attributes of chaotic functions. This adaptability positions the method to address complex optimization challenges across various fields. Beside, the experiment show that enhanced method has a better result by using Firefly Algorithm classifier and the TF-IDFC-RF feature extraction.

关键词： Spirals Supervised learning Metaheuristics Morphology Syntactics Feature extraction whale optimization algorithms Classification algorithms Information technology

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Research on Personalized Recommendation Based on Improved whale optimization Algorithm

Research on Personalized Recommendation Based on Improved Wh...

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Computer Technology and Information Science (ISCTIS), International Symposium on

作者： Jun Jiang Wenyang Qing Jun Hu Fazhong Hou School of Medical Humanities and Information Management Hunan University of Medicine Huaihua China

ISBN: (数字)9798350354560

ISBN: (纸本)9798350354577

In order to solve the problem of insufficient information mining and low degree of personalization due to the single data dimension of cognitive diagnosis test question recommendation, this paper proposes a personalized exercise question recommendation method based on multi-dimensional data cognitive diagnosis optimization model. Firstly, the improved whale optimization algorithm with adaptive probability distribution is used to solve the problem that the initial clustering center of the K-means algorithm is sensitive and easy to fall into the local optimum, and secondly, the optimized k-means algorithm was used to cluster the exercises to generate a specific group of test questions. Then, the students' answers to specific test groups were recorded to form a multi-dimensional test question set. Finally, based on the multi-dimensional question bank, the method proposed in this paper and the traditional GDINA cognitive model algorithm are used to test the average accuracy of student sampling, and the results show that the algorithm proposed in this paper has stronger information mining ability and is more suitable for accurate recommendation of personalized test question resources matching learning needs and abilities.

关键词： Information science Accuracy optimization models Clustering algorithms whale optimization algorithms Probability distribution Data models Data mining optimization Periodic structures

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A Fault Forecasting Approach Using Two-Dimensional optimization

A Fault Forecasting Approach Using Two-Dimensional Optimizat...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Mohsen Tajgardan Reza Khoshkangini Abbas Orand Mahdi Rabbani Faculty of Electrical and Computer Engineering Qom University of Technology Qom Iran Department of Computer Science and Media Technology Internet of Things and People (IOTAP) Malmö University Malmo Sweden Arriver Software AB Qualcomm Company Linköping Sweden Canadian Institute for Cybersecurity University of New Brunswick NB Canada

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

Data preparation is crucial in every machine learning approach, particularly when applied to detecting claims in the automotive industry. The challenge of managing highdimensional feature spaces and imbalanced data becomes even more pronounced with the proliferation of IoT devices, which generate vast amounts of data over time. Machine learning models trained on such imbalanced datasets often yield unreliable and inaccurate predictions. Thus, addressing these issues during the data pre-processing phase is critical. In this paper, we introduce a novel two-dimensional optimization (TDO) strategy to tackle the problem of imbalanced data in fault detection, specifically in the context of IoT-enhanced automotive systems. We leverage a heuristic optimization technique known as the Genetic Algorithm to simultaneously reduce both the number of data point tuples and the feature space. Additionally, we evaluate the effectiveness of two-dimensional reduction using Particle Swarm optimization (PSO) and whale optimization algorithms. Our empirical results, derived from data collected from thousands of IoT-equipped vehicles, demonstrate the promise of our proposed methods.

关键词： Industries Fault detection Machine learning Predictive models whale optimization algorithms Internet of Things Particle swarm optimization optimization Automotive engineering Genetic algorithms

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Metaheuristic Approaches for Energy Efficiency and Network Lifetime optimization in Wireless Sensor Networks

Metaheuristic Approaches for Energy Efficiency and Network L...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： A.S. Sai Puneeth Theja S. Swarnalatha B. Shoban Babu Department of ECE Sri Venkateswara University College of Engineering Tirupati A.P. India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

This paper focuses on traditional and natureinspired metaheuristic algorithms, such as Low-Energy Adaptive Clustering Hierarchy (LEACH), Particle Swarm optimization (PSO) in Wireless Sensor Networks (WSNs). The evaluation focuses on key parameters like network lifetime, node clustering, and energy consumption levels. Results indicate that PSO consistently outperform the LEACH algorithm in these metrics However, the Adaptive whale optimization Algorithm (AWOA) is competitive with the PSO algorithm with its results leaning towards on the better side. The study complements related research on the application of swarm intelligence in WSN by focusing on routing optimization, energy aware protocols and centralized clustering of nodes.

关键词： Measurement Wireless sensor networks Protocols Visual analytics Metaheuristics Clustering algorithms Routing Energy efficiency whale optimization algorithms Particle swarm optimization

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An Improved IIR Filter Design using Artificial Rabbits optimization Algorithm and its FPGA Implementation

An Improved IIR Filter Design using Artificial Rabbits Optim...

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Devices for Integrated Circuit (DevIC)

作者： Subhadeep Mondal Sajal Sarkar Raina Modak Aich Supriya Dhabal Palaniandavar Venkateswaran Dept. of ETCE Jadavpur University Kolkata West Bengal India Dept. of ECE Netaji Subhash Engineering College Kolkata West Bengal India

ISBN: (数字)9798350391107

ISBN: (纸本)9798350391114

This paper presents a meta-heuristic optimization algorithm known as Artificial Rabbits optimization (ARO) to get the optimal set of infinite impulse response (IIR) filter coefficients. Application of the ARO algorithm to design two real-life examples is used for performance testing. The proposed algorithm is simulated to get the coefficients of different types of linear and non-linear (with noise and without noise) problems with similar order and lower order, and the simulated results are compared with few recent optimization algorithms such as Salp Swarm Algorithm (SSA), whale optimization Algorithm (WOA), and Dragonfly Algorithm (DA). The proposed algorithm is much faster and efficient than the numerous standard algorithms. The Proposed ARO algorithm demonstrates a lower Mean Square Error (MSE) and superior convergence speed as compared to the other optimization techniques examined herein. FPGA implementation is performed to design IIR filters where ARO shows better performance.

关键词： Rabbits Noise Metaheuristics IIR filters Skin whale optimization algorithms Internet of Things Field programmable gate arrays Convergence Testing

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Fopid Controllers Based Forward DC to DC Converter With Modified Osprey optimization Algorithm for MPPT of PV System Under Partial Shading Conditions

Fopid Controllers Based Forward DC to DC Converter With Modi...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Naveen Kumar Bind K. Krishnaveni Burada Sankara Rao Vikrant Singh Sadu Munisekhar Neel Kamal Ch. Lenin Babu Department of Electrical Engineering JSS Academy of Technical Education Noida Uttar Pradesh India Department of EEE Chaitanya Bharathi Institute of Technology Hyderabad Telangana India CPGC Pvt. Ltd and ERG Foundation Anakapalli Visakhapatnam Andhra Pradesh India Department of Electrical Engineering O.P. Jindal University Raigarh Chhattisgarh India Electrical and Electronics Engineering Sri Venkatesa Perumal College of Engineering and Technology (SVPCET) Puttur India JNTU Ananthapuramu India Electrical & Electronics Engineering School of Engineering & Technology IIMT University Meerut U.P. India Department of EEE RSR Engineering College Kavali India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Everywhere, electric power is needed for a variety of functions and tasks. Creating electric power through renewable resources, like solar energy, is a practical way to solve a lot of issues. Typically, photovoltaic modules use solar radiation to generate electricity. To guarantee maximum usage or optimal efficiency, a photovoltaic system must be equipped with a maximum power point tracking converter (MPPTC). In this paper, a DC to DC forward converter is chosen to function as MPPTC. For MPPTC under uniform sun irradiances, a variety of traditional methods are available, such as incremental conductance, perturb, and observe. However, trees, shade, dust, birds, clouds, and other factors will cause photovoltaic modules - of which many are coupled to make a power production unit - to receive non-uniform irradiances. The system enters a partial shading condition (PSC) as a result of this event. Conventional algorithms may be failed to approach the maximum utilization point to increase energy extraction under PSC. Therefore, to operate the power unit at its optimal usage under the situation of PSC, an optimization method is needed. In this study on MPPTC, an optimization method called the “Modified Osprey optimization Algorithm (MOOA)” is designed to gather more energy during PSCs. This research also compares MOOA, Modified Invasive Weed optimization (MIWO), Modified Grey Wolf optimization (MGWO), and whale optimization Algorithm (WOA). OPALRT technology is used to create the Hardware-in-the-Loop, which gathers diverse outcomes for analysis under different operations.

关键词： Maximum power point trackers Renewable energy sources optimization methods Solar energy whale optimization algorithms Power systems Solar panels Solar radiation Voltage control Sun

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Beluga whale optimization for Effective Energy Management in Microgrids with Electric Vehicle Charging in Smart Distribution Networks

Beluga Whale Optimization for Effective Energy Management in...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Narendra Pothula Nagasrisaihari Sunkara Vanitha R Thiyagesan M H. Mickle Aancy Balasubbareddy Mallala Department of Mechanical Engineering VNR Vignana Jyothi Institute of Engineering & Technology Hyderabad Telangana India Department of Electrical and Electronics Engineering Aarupadai Veedu Institute of Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering R.M.K Engineering College Chennai Tamil Nadu India Department of Master of Business Administration Panimalar Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad Telanagana India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

EM in MGs with EV charging refers to the special mainstream of providing electricity and continuously managing and balancing energy supply, load demand and integrating prosumer elements to maintain proper grid stability and efficiency. There are several disadvantages in the incorporation of EVs in to MGs and smart distribution networks. They are the high initial investment costs which accompany infrastructure improvements like updating charging stations and smart grid technologies may hinder implementation. In this paper, we present a new approach of Beluga whale optimization (BWO) incorporating electric vehicle charging stations (EVCS) for EM in MGs. The primary task that the given approach addresses is the optimization of system utilization and LCOE reduction. The BWO is applied for the performance enhancement of load scheduling of MGs with incorporated EV charging in smart distribution grids. The structure of BWO approach is also established on MATLAB platform, and their performance is compared with other algorithms including Artificial Neural Network (ANN), Shell Game optimization (SGO), Multi-Objective Improved arithmetic optimization Algorithm (MOIAOA) and Young's double Slit-Experiment Optimizer (YDSEO). The presented BWO method obtains LCOE equal to 22,173$/kWh to prove its efficiency over existing methodologies for planning and designing energy systems.

关键词： Energy consumption Renewable energy sources Electricity Artificial neural networks Distribution networks Power system stability Electric vehicle charging whale optimization algorithms MATLAB Energy management

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