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检索条件"主题词=whale optimization algorithms"

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Vehicle charging station location based on improved whale optimization algorithm

Vehicle charging station location based on improved whale op...

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International Conference on Computer Engineering and Applications (ICCEA)

作者： Zhaodi Li Qingqing Liu Kaixuan Yu Department of Computer and Information Engineering Henan University Kaifeng China Department of Computer Science and Art Henan Light Industry Vocational College ZhengZhou China State Key Laboratory of Shield Machine and Boring Technology ZhengZhou China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

In order to better explore a method for solving the problem of electric vehicle charging station location planning, and improve the shortcomings of whale algorithm such as weak stability, sometimes slow convergence speed and easy to fall into local extremum, a whale optimization algorithm with quasi-opposition-based learning and enhanced search mechanism was proposed. Then, the algorithm flow is given, and the CEC2017 test function set is simulated by six representative algorithms in multiple dimensions. The results show that the convergence speed, optimization accuracy and solution stability of the improved algorithm are significantly improved, and it has good convergence performance. In order to solve the problem of electric vehicle public charging station location and service area division, an improved whale algorithm and Voronoi diagram are used to jointly solve the location planning of charging station, so as to achieve the purpose of global optimization. The application results of the last example show that the improved whale algorithm can effectively solve the charging station location problem and has superior optimization performance.

关键词： Accuracy Urban areas whales Charging stations Search problems whale optimization algorithms Stability analysis

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An Intelligent Solution to Fractional Order Controller Tuning Problem Using whale optimization Method

An Intelligent Solution to Fractional Order Controller Tunin...

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Silchar Subsection Conference (SILCON), IEEE

作者： Sayan Kundu Mahadeb Soo Soumadip Chatterjee Soham Dey Dept. of Electrical Engineering Dr. B.C. Roy Engineering College Durgapur India

ISBN: (数字)9798331540821

ISBN: (纸本)9798331540838

In this paper, design and implementation of various fractional order controllers designed using an artificial intelligence based modern meta-heuristic optimization algorithm has been demonstrated for solving a real world control problem of the magnetic levitation system (Maglev). fractional order calculus (FOC) plays a significant role in control theory, offering several advantages in modeling and controlling complex systems. FOC has gained a lot of research interest as a state-of-the-art control system design procedure. Basically fractional order controllers are a sub-application of FOC which provide accurate modelling, enhanced system performance, better stability and improved robustness compared to traditional integer order controllers. Therefore this study is concentrated on design and comparative analysis of fractional order (FO) lead, lag-lead and PID controllers based on a newly developed optimization algorithm known as whale optimization Algorithm (WOA). The objective function for controller parameter optimization is formulated using a standard error based performance metric namely integral square error (ISE). The simulation analysis signifies that the optimum performance is exhibited by the proposed FOPID controller.

关键词： Trajectory tracking System performance Stability criteria Metaheuristics Lead Control systems whale optimization algorithms Tuning Standards Magnetic levitation

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An Improved Beluga whale optimization Algorithm by Collaborative Strategies for Multi-Threshold Image Segmentation

An Improved Beluga Whale Optimization Algorithm by Collabora...

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International Conference on Computer Supported Cooperative Work in Design

作者： Mengran Liu Hui Xu Qinyue Wu Chenbing Dong School of Computer Science Hubei University of Technology Wuhan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Multi-threshold segmentation techniques usually rely on exhaustive search, leading to an exponential growth in computational complexity with the increase in the number of thresholds. The combination of swarm intelligence and image segmentation is a promising approach to improve segmentation efficiency and reduce computational cost. Thus, this paper then proposes an Improved Beluga whale optimization (IBWO) algorithm incorporating collaborative strategies for multi-threshold image segmentation. As for the proposed IBWO algorithm, the following collaborative strategies are utilized. The logistic chaos mapping strategy can increase the randomness of the initial beluga population, the dynamic nonlinear equilibrium factor strategy can better balance the exploratory and exploitation phases, and the dynamic pinhole imaging strategy can increase the diversity of the population and prevent the population from falling into a local optimum. The objective function is minimum cross entropy, and experiments use benchmark images at different threshold levels to evaluate the method's performance and compare the IBWO algorithm with several other classical algorithms. The experimental results show that the proposed IBWO algorithm outperforms the comparative methods in all three image quality metrics, which are PSNR, SSIM, and FSIM.

关键词： Measurement Image segmentation Heuristic algorithms Sociology Collaboration Linear programming whale optimization algorithms

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whale Algorithm-Based optimization of Orderly Charging Strategies for Electrical Vehicles

Whale Algorithm-Based Optimization of Orderly Charging Strat...

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Energy Engineering and Power Systems (EEPS), International Conference on

作者： Jianbin Lu Ning Wu Weidong Chen Rui Lin Jing Xiao Electric Power Research Institute of Guangxi Power Grid Co. Ltd Nanning China Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Nanning China Guangxi Power Grid Co. Ltd Nanning China

ISBN: (数字)9798350375138

ISBN: (纸本)9798350375145

As of October 2023, the number of new energy vehicles in China has surpassed 18 million. The surge in electrical vehicle (EV) numbers has led to significant adverse impacts on the power grid due to the charging load they create. Unorderly large-scale charging of EVs, in particular, exacerbates the problem by adding additional peaks to the existing demand peaks, severely affecting the stability of the power system. Therefore, it holds paramount significance in guiding EVs toward orderly charging. This paper commences by analyzing the impact of unorderly EV charging on the power grid, culminating in an orderly charging strategy. This strategy clearly defines the expected outcomes through an objective function and ensures feasibility and effectiveness through constraint conditions. The orderly charging strategy undergoes optimization with the aid of an enhanced whale optimization Algorithm (WOA), followed by corresponding case studies. This paper considers multiple objectives and constraints in the construction of the orderly charging strategy and effectively optimizes the strategy using an enhanced optimization algorithm, thereby facilitating orderly charging for EV users and reducing fluctuations in grid load.

关键词： Costs whales Power system stability Minimization Linear programming Stability analysis Power grids whale optimization algorithms Surges optimization

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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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