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检索条件"主题词=Whale Optimization Algorithms"

共 402 条记录，以下是141-150 订阅

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An Overview of optimization Challenges in Electric Mobility Charging Stations and Driver Behavior in Congested Cities

An Overview of Optimization Challenges in Electric Mobility ...

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International Smart Grid Workshop and Certificate Program (ISGWCP)

作者： Juliana Bermúdez Juan Esteban González David Celeita School of Engineering Science and Technology Universidad del Rosario Bogotá Colombia Faculty of Engineering Universidad de la Sabana Chía Colombia

ISBN: (数字)9798350361612

ISBN: (纸本)9798350361629

As the electric transportation industry expands, is imperative to face the implications of this new infrastructure. In fact, this paper intends to generate a comprehensive overview of the challenges associated with the implementation of an electric vehicle charging system in Colombia’s capital city Bogotá. Given the complexity of the problematic, it is divided into five main components: optimal location for charging stations, electric vehicles’ routing algorithms, scheduling and queue management, driver’s behavior and modeling of dynamical systems. This division, enables the analysis of methodologies such as swarm optimization algorithms, genetic-based algorithms, whale optimization algorithms, spherical fuzzy sets, Voronoi diagrams, dynamic weighted graphs, Lyapunov functions, generalized linear models, convolutional networks, Markov chains and some others in terms of how appropriate they are for providing a solution. Finally, a preliminary model to represent this dynamic is introduced along as further work taking as a starting point the representation of the system.

关键词： Heuristic algorithms Transportation industry Urban areas Charging stations Routing whale optimization algorithms Smart grids

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An Improved Beluga whale optimization Algorithm Based on Chaotic Opposition-Based Learning and Adaptive Weighting Factor

An Improved Beluga Whale Optimization Algorithm Based on Cha...

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New Trends in Computational Intelligence (NTCI), International Conference on

作者： Bingbing Zhang Jingqing Jiang Jiazhi Song College of Mathematics Science Inner Mongolia Minzu University Inner Mongolia China College of Computer Science and Technology Inner Mongolia Minzu University Inner Mongolia China

ISBN: (数字)9798331517021

ISBN: (纸本)9798331517038

Beluga whale optimization (BWO) algorithm faces challenges such as slower convergence, lower accuracy, and instability for dealing with unimodal functions. To tackle these problems, we propose an enhanced version of Beluga whale optimization Algorithm (ABWO). ABWO algorithm incorporates chaotic Opposition-Based Learning (OBL) and adaptive weighting factor. We fuse Logistic and Tent chaotic mappings with OBL to ensure an even distribution of individuals in the initial solution space during initialization. Consequently, the population diversity is increased and the quality of the initial solution is better. The adaptive weighting factor $F$ is introduced in the exploitation phase. It increases and then decreases with each iteration. This adjustment enhances the guiding effect of the global optimal position on individual position updates in the early stage of BWO. It also enables the algorithm to execute more effective global searches in the middle stage. Meanwhile, the algorithm converges more quickly in the late stage. Overall, the solution accuracy and convergence stability of the algorithm are improved. On nine unimodal test functions, ABWO has better optimization and convergence performance compared with six basic algorithms. It also outperforms three improved beluga whale optimization algorithms proposed by other scholars.

关键词： Accuracy Fuses Market research whale optimization algorithms optimization Faces Computational intelligence Convergence Logistics

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An Optimal Planning of Energy Storage System in Radial Distribution Network Using Aquila optimization Algorithm

An Optimal Planning of Energy Storage System in Radial Distr...

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Data Science and Information System (ICDSIS), IEEE International Conference on

作者： Myasar Mundher Adnan A.V.V. Sudhakar S. Arun Kumar G K Madhura S. Kaliappan Department of computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical and Electronics Engineering SR University Warangal Telangana India Department of Electrical and Electronics Engineering Sona College of Technology Salem India Department of Artificial intelligence and Machine learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Mechanical Engineering Amity University Dubai United Arab Emirates

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

Nowadays smart grids are emerging rapidly for their efficiency in operating modern distribution systems. Energy storage systems (ESSs) play a key role in achieving the objectives of smart grids, facilitating the combination of renewable sources and enhancing system reliability. Aquila optimization Algorithm (AOA) is proposed for optimal placement and sizing of ESS within the IEEE Radial Distribution System (RDS) to minimize power losses while maintaining voltage magnitude. This method is used to achieve improvements in loss reduction and voltage profile compared to conventional Analytical Index method and whale optimization Algorithm (WOA). The experimental results indicate that if ESS are sized and installed correctly, the overall losses in the radial system are decreased. The suggested AOA achieved a loss reduction rate of 62.58% and a voltage profile of 79.87 kW in enhancing the performance of distribution systems within smart grid frameworks.

关键词： Renewable energy sources Network topology whale optimization algorithms Smart grids Topology Planning Reliability

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Big Data Clustering using Ghost Opposition Learning based Crayfish optimization Algorithm

Big Data Clustering using Ghost Opposition Learning based Cr...

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Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Yifeng Wang Yuxia Du Shandong Institute of Commerce and Technology Jinan China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Traditional clustering methods often struggle with the sheer scale and complexity of big data. Clustering algorithm of big data models by efficiently handling large datasets, making them more scalable and capable of processing diverse data types. In this big data Clustering technique-based Swarm intelligence optimization various of model are created but still the optimization algorithms are trapping in local optima and its takes huge time for finding the global solution. Addressing these issues and overcome in this paper, proposed the Crayfish optimization Algorithm by using Ghost Opposition-based Learning (GOL-COA) Strategy and environment update technique for enhancing exploitation phase and increasing Exploration convergence speed of optimization, and avoiding local optima problem. The model is evaluated with the accuracy and F1-score metrices and experiment did on various types of datasets. The numerical validation results showing accuracy of 97.56% in LD dataset, and F1 score of 98.10% in LD dataset. This model compares to the other recent year optimization algorithms such as Particle Swarm optimization (PSO), whale optimization (WOA), Ant Colony optimization (ACO), Sand Cat Swam optimization (SCSO), and Werewolf optimization algorithms (WOA).

关键词： Accuracy Clustering algorithms Big Data Data models whale optimization algorithms Numerical models Particle swarm optimization Computer security optimization Convergence

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Lifetime Enhancement of Heterogeneous WSNs by whale optimization Algorithm

Lifetime Enhancement of Heterogeneous WSNs by Whale Optimiza...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Neeraj Kaushik Amit Singhal Ashish Pandey Dept. of Computer Science and Engineering Monad University Hapur (UP) India Dept. of Electronics and Communication Engineering GCET Greater Noida (UP) India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Wireless sensor networks (WSNs) often trust on batteries for power, which cannot frequently be recharged or replaced easily across several applications. Hence, in this work, a novel approach using, whale optimization Algorithm (WOA) to optimize routing and power constraints, aiming to prolong the battery life of sensor nodes within the network. This purposed technique focuses on energy-efficient clustering and routing strategies to prolong the operational lifespan of individual nodes. Comparative analysis against existing optimization techniques in WSNs proves the superiority of the proposed WOA-based approach, particularly in achieving prolonged lifetimes under diverse power constraints. To authenticate its robustness, the approach is proved across different base station placements and varying populations of sensor nodes and gateways.

关键词： Wireless communication Wireless sensor networks Clustering algorithms Logic gates Routing whale optimization algorithms Robustness Energy efficiency Batteries optimization

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Key Parameters Prediction for Intelligent Control and Energy optimization Based on MIC-BWO-XGBoost

Key Parameters Prediction for Intelligent Control and Energy...

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Cyber-Energy Systems and Intelligent Energy (ICCSIE), International Conference on

作者： Changyao Lv Yaqi Wang Yudong Chen Zhuo Yu Guoliang Hu Hongliang Wu Shuai Li Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Shanghai Aircraft Manufacturing Co. Ltd. Shanghai China College of Information Engineering Shenyang University of Chemical Technology Shenyang China

ISBN: (数字)9798350373318

ISBN: (纸本)9798350373325

In the aviation industry, anodic oxidation serves as a core process for surface modification of aluminum alloy components, albeit accompanied by high energy consumption. Accurate prediction of key parameters can offer decision support for the intelligent control and energy optimization of anodic oxidation. However, the limited sample size resulting from the high cost of data collection and the complex nonlinear patterns caused by the synergy of multiple factors pose a challenge in accurately predicting key parameters. Therefore, we propose a key parameters prediction method based on the Maximum Information Coefficient (MIC), Beluga whale optimization algorithm (BWO), and XGBoost algorithm. Considering the adverse impact of low correlation parameters on prediction accuracy, we first use MIC to extract features from the original data. Then, XGBoost is used to integrate multiple decision trees to ensure the strong fitting ability for nonlinear relationships. Compared to deep learning models, XGBoost performs well even in a limited sample size. In addition, considering XGBoost is prone to overfitting training data as an ensemble learning algorithm, we employ the BWO to optimize four key parameters of the model that are difficult to choose, thereby enhancing model's generalization. The model's performance is evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with values of 0.04388, 0.09410, and 0.20730%, respectively. The experimental results demonstrate that the proposed method outperforms traditional approaches, enabling precise prediction of key parameters and holding significant value for the intelligent control and energy optimization of anode oxidation processes.

关键词： Microwave integrated circuits Accuracy Process control Prediction algorithms Feature extraction Data models whale optimization algorithms Oxidation optimization Intelligent control

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Optimal Placement of Distributed Generators and Electric Vehicles Using whale optimization Algorithm (WOA)

Optimal Placement of Distributed Generators and Electric Veh...

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Innovative Sustainable Technologies for Energy, Mechatronics, and Smart Systems (ISTEMS), International Conference on

作者： Rinchen Zangmo Suresh Kumar Sudabattula Sachin Mishra Nagaraju Dharavat School of Electronics and Electrical Engineering Lovely Professional University Punjab India

ISBN: (数字)9798350384246

ISBN: (纸本)9798350384253

Integrating Distributed Generation (DG) sources and Electric Vehicles (EVs) into radial distribution systems (RDS) is a promising approach to enhancing power system efficiency and reliability. This trend is driven by the deregulation of the electric power sector and technical constraints in extending distribution and transmission networks to some areas. By carefully selecting the optimal location and sizing for DG sources and electric vehicles (EVs), it is possible to minimize system losses, improve voltage profiles, and enhance overall reliability. This study suggests the integration of multiple DGs and EV s into the system and how the PLoss get affected along with voltage profiles. However, the PLoss increase if the number of DGs exceeds their optimal level. This study integrates 3 DGs and 3 EVs into the RDS separately. The location of the DGs and EVs are obtained with the help of the voltage stability index, and sizes were obtained using the whale optimization Algorithm (WOA). It is implemented into the IEEE 33 test system to verify its robustness and effectiveness. The results show a loss reduction of 41.96%, 57.52%, and 64.73% for the IEEE bus system with the integration of one, two and three DGs, respectively and a slight increase in power loss concerning EV integration as the system load increases. In addition, the voltage stability index is improved significantly for DGs but was affected in the case of EVs.

关键词： Vehicle-to-grid Electric potential System performance Stability criteria Voltage Power system stability whale optimization algorithms

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Bi-Level Planning for Distributed Power Supply Using the Improved whale optimization Algorithm

Bi-Level Planning for Distributed Power Supply Using the Imp...

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China International Electrical and Energy Conference (CIEEC)

作者： Jinfeng Wang Mengxue Xu School of Electrical and Information Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

Optimizing distributed power supply planning is essential to enhancing the impact of distributed power supply on the secure and cost-effective operation of the distribution network. This study establishes a two-layer programming model and selects wind power, solar, and micro-gas turbines as distributed power sources to be scheduled. This study aims to improve the whale algorithm by utilizing tent mapping, inertial weight, and nonlinear shrinkage factor to address issues like slow convergence and delayed jumping out of local solutions. This study solves the proposed model on the IEEE-33 node system using the enhanced whale algorithm and compares the outcome with other techniques. The enhanced whale algorithm offers the most noticeable improvement in node voltage and the lowest annual comprehensive cost, according to the data.

关键词： Costs Power supplies whales Distribution networks Wind power generation whale optimization algorithms Stability analysis

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High-performance quarter car active suspension system using PID controller tuned by the dingo optimization algorithm

High-performance quarter car active suspension system using ...

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Power Electronics and Control (SEPOC), Seminar on

作者： Thiago Carvalho Bittencourt Joab Tavares Fagundes Paulo Jefferson Dias de Oliveira Evald Sul-Riograndense Federal Institute Pelotas Brazil Intelligent Systems and Control Group Federal University of Pelotas Pelotas Brazil

ISBN: (数字)9798350379419

ISBN: (纸本)9798350379426

This paper presents a novel approach for tuning a proportional-integral-derivative (PID) controller applied to the active suspension system of an electric vehicle using the dingo optimization algorithm (DOA). The DOA is utilized as the core of a sophisticated controller tuning method and follows systematic rules to provide optimal gains to the PID, enhancing vehicle stability. As an outcome, the security and the comfort of the driver and passengers are increased, once the optimized controller can minimize the vehicle balance on irregular land. The active suspension model is based on a quarter-car representation. Three scenarios were considered in the simulation, including a flat road, a speed bump, and a road with a hole. The results demonstrate that the DOA-tuned PID controller effectively minimizes tracking errors, ensuring stable and precise control of the suspension system without excessive control effort. Another meta-heuristic approach was used for comparison, the whale optimizer algorithm (WOA). The proposed controller presented superior performance, not developing the oscillatory behavior of the WOA-tuned algorithm. Moreover, the settling time using the proposed control approach is null. In this sense, the developed control system enhances the comfort and security of the driver and passengers.

关键词： Suspensions (mechanical systems) Roads Metaheuristics Electric vehicles whale optimization algorithms Stability analysis Security Transient analysis Tuning Vehicles

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River Water Salinity Prediction Utilized a Social Learning and Wavelet Mutation whale optimization Algorithm

River Water Salinity Prediction Utilized a Social Learning a...

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Software, Systems and Information Technology (SSITCON), International Conference on

作者： Kudaravalli Sita Rama Prasad S. Meenakshi Sundaram Merwa Alhadrawi Rana Veer Samara Sihman Brahmatej Rupavath K. Saranya Department of Civil Engineering V. R. Siddharta Engineering College Vijayawada India Department of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computer Science Indiana Wesleyan University Marion Indiana USA Department of Electronics and Communication Engineering Sona college of Technology Salem India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

River water demand forecasting can contribute significantly to water resource management. Analyzing river water salinity prediction involves utilizing datasets on water quality, which can be influenced by various factors, including population growth, economic development, and living standards. However, there are challenges such as susceptibility to falling into local optima, dealing with large input spaces and the increased dimensionality of computational processes, especially with initial clustering input values. To address these challenges, salinity prediction utilizes the Adaptive Neuro Fuzzy Inference System (ANFIS) combined with the Social Learning and Wavelet Mutation whale optimization Algorithm (SMWOA) to estimate water resource demand. The proposed method integrates the SMWOA mutation strategies to enhance linear probability and improve global search capabilities. Results demonstrate that the proposed method ANFIS-SMWOA, achieves superior performance metrics including the Nash-Sutcliffe Efficiency Coefficient (NSE) of 0.996, Root Mean Square Error (RMSE) of $\mathbf{0. 3 2 9 5}$, Coefficient of Determination $R^optimization$ of $\mathbf{0. 9 4 1}$, Normalized RMSE (NRMSE) of $\mathbf{0. 1 2 8,}$, Modified Index of Agreement (md) and Kling-Gupta efficiency (KGE) of 0.983 on water quality datasets. These outcomes confirm the better detection and classification performance of the proposed method compared to other existing methods, such as Relevance Vector Machine (RVM), Improved Grasshopper optimization Algorithm (IGOA), Simulated Annealing (SA), and the Mayfly optimization Algorithm (MOA).

关键词： Fuzzy logic Salinity (geophysical) Software algorithms Water quality Prediction algorithms Inference algorithms whale optimization algorithms Rivers Water resources optimization

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