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检索条件"主题词=Whale optimization algorithms"

共 378 条记录，以下是91-100 订阅

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Techno-Economic optimization of Grid Connected Electric Vehicle Charging Station: A Case Study of Riyadh, Saudi Arabia

Techno-Economic Optimization of Grid Connected Electric Vehi...

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International Conference on Adaptive Science & Technology (ICAST)

作者： Mohd Bilal Pitshou N. Bokoro Gulshan Sharma Department of Electrical Engineering Technology University of Johannesburg Johannesburg South Africa

ISBN: (数字)9798350385403

ISBN: (纸本)9798350385410

This research evaluates the integration of renewable energy sources with grid-connected systems to power electric vehicle charging stations (EVCSs) in Riyadh, Kingdom of Saudi Arabia (KSA), underscoring its importance for sustainable development. An eco-friendly EVCS model is proposed and apply a novel metaheuristic optimization technique, the Enhanced Bald Eagle Search Algorithm (EBESA), to determine the optimal sizing of system components. Our objective is to minimize the total net present cost (TNPC) and the levelized cost of energy (LCOE) while ensuring reliability as quantified by the loss of power supply probability (LPSP). The performance of the EBESA is compared with alternative algorithms such as the Bald Eagle Search Algorithm (BESA), whale optimization Algorithm (WOA), and Particle Swarm optimization (PSO), assessing their efficacy in system component sizing. Our findings indicate that the optimal EVCS configuration is a solar photovoltaic/wind turbine (PV/WT) grid-tied system, achieving a LCOE of $0.0796/kWh, a TNPC of $102,042. our study aims to guide policy-making and investment in the expansion of EV charging infrastructure, with broader implications for developing countries.

关键词： Photovoltaic systems Renewable energy sources Power supplies Electric vehicle charging whale optimization algorithms Stakeholders Reliability Sustainable development Particle swarm optimization Turbines

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Enhanced On-Off Control for Wind Energy Conversion System Electromagnetic Torque Ripple Minimization based on whale optimization Algorithm

Enhanced On-Off Control for Wind Energy Conversion System El...

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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： Amel Bouchemha Oussama Brahmi Sami Kahla Chaouki Ghenai LAVIA Laboratory Echahid Cheikh Larbi Tebessi University Tébessa-Algeria Mines Laboratory Echahid Cheikh Larbi Tebessi University Tébessa-Algeria Research Centre in Industrial Technologies CRTI Algiers Algeria Department of Sustainable and Renewable Energy Engineering College of Engineering Sharjah United Arab Emirates

ISBN: (数字)9798350372632

ISBN: (纸本)9798350372649

An improved On-Off control method for wind energy conversion systems (WECS) that makes use of a Squirrel cage induction generator (SCIG) and the whale optimization Algorithm (WOA) is the main focus of this research study. The On-Off control scheme is known for its simplicity and robustness against disturbances and model uncertainties. However, it can lead to the chattering phenomenon, which can be detrimental to the lifespan of the wind turbine. To address this issue, an Enhanced On-Off controller is proposed in this study. The objective is to reduce the electromagnetic torque ripple by tuning the Enhanced Controller with the whale optimization Algorithm. Even though the chattering issue was reduced by the proposed controller, it was not totally eliminated. To assess the performance of the proposed controller, a wind profile based on Von Karman spectrum is used. This later is defined by the International Electrotechnical Commission (IEC) standard. The obtained results show that the proposed approach outperforms the improved On-Off controller.

关键词： Shafts Vibrations Uncertainty Induction generators Wind speed whale optimization algorithms Wind turbines

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Enhancing Intrusion Detection Systems: A Comparative Study using whale optimization Algorithm and Hawks optimization Algorithm

Enhancing Intrusion Detection Systems: A Comparative Study u...

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International Conf on Electrical and Electronic Engineering (ICEEE)

作者： Mosleh M. Abualhaj Sumaya N. Al-Khatib Ali Al-Allawee Alhamza Munther Mohammed Anbar Department of Networks and Cybersecurity Al-Ahliyya Amman University Amman Jordan IRIMAS University of Haute Alsace Colmar France IT Department University Technology and Applied Sciences Sur Oman National Advanced IPv6 Centre (NAv6) Universiti Sains Malaysia Penang Malaysia

ISBN: (数字)9798350362541

ISBN: (纸本)9798350362558

Intrusion Detection Systems (IDS) play a pivotal role in safeguarding computer networks against malicious activities. This research explores the efficacy of two optimization algorithms, namely whale optimization Algorithm (WOA) and Hawks optimization (HHO), for feature selection to enhance the performance of IDS. The NSL-KDD dataset is utilized as a benchmark to evaluate the impact of selected features on classification accuracy. The study employs the Random Forest (RF) classification algorithm to evaluate the effectiveness of feature selection methods. Implementation is conducted in Python, leveraging its versatile libraries and frameworks. Comparative analysis reveals that when integrating the selected features, RF attains superior results compared to utilizing features separately for binary and multiclass classifications. Results indicate that RF with HHO achieves an impressive accuracy of 98.02%, showcasing the prowess of HHO in enhancing IDS performance. Meanwhile, RF with WOA achieves a commendable accuracy of 97.81%.

关键词： Radio frequency Accuracy Refining Intrusion detection Benchmark testing Feature extraction NSL-KDD whale optimization algorithms Classification algorithms optimization

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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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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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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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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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