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检索条件"主题词=Whale optimization algorithms"

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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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Enhancing the Puma Optimizer Algorithm for optimization Problems

Enhancing the Puma Optimizer Algorithm for Optimization Prob...

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International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON)

作者： Sakkayaphop Pravesjit Krittika Kantawong School of Information and Communication Technology University of Phayao Phayao Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

This paper proposes an enhanced Puma Optimizer Algorithm for solving optimization problems. The Rao Algorithm is incorporated and refined within the exploration phase of the Puma Optimizer, incorporating novel procedures to improve position updates. The proposed algorithm is evaluated on six benchmark functions. An evaluation results show in comparison to Differential Evolution (DE), Intersection Mutation Differential Evolution (IMDE), the whale optimization Algorithm (WOA), and the Sand Cat Swarm optimization Algorithm (SCSO). Computational results demonstrate that the proposed algorithm achieves optimal solutions for two of the six benchmark functions. Furthermore, it consistently outperforms the other four algorithms across the test suite. These findings highlight the potential for further advancements in the Puma Optimizer and demonstrate its effectiveness in addressing continuous step functions, multimodal functions, and discontinuous step functions, achieving performance that is competitive with or surpasses existing state-of-the-art methods.

关键词： Benchmark testing whale optimization algorithms Particle swarm optimization optimization

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Semantic QoS-aware Resource Allocation for IoV Computation Offloading using Layer Enhanced whale optimization Algorithm

Semantic QoS-aware Resource Allocation for IoV Computation O...

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International Conference on Computer and Communications (ICCC)

作者： Caitong Tang Ningbo Zhang Key Laboratory of Universal Wireless Communications Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

In the Internet of Vehicles, computation task offloading is essential to meet increasing computational demands. This paper proposes a resource allocation algorithm based on an enhanced whale optimization Algorithm (WOA) for semantic communication, dynamically allocating resources based on the semantic quality of tasks. We develop a Semantic-Aware Layer Enhanced whale optimization Algorithm (LEWOA) that improves multi-dimensional resource allocation by considering the Quality of Service (QoS) of semantic data. Simulations using the YOLO-V8 framework and HRNet are performed to validate the superiority of the proposed algorithm.

关键词： Heuristic algorithms Simulation Semantic segmentation Quality of service Semantic communication whale optimization algorithms Resource management Vehicle dynamics optimization Internet of Vehicles

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Improving the Application of whale optimization Algorithm in PID Parameter optimization for Linear Motor Control System of Wire Bonding Machine XY Motion Platform

Improving the Application of Whale Optimization Algorithm in...

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International Conference on (ICEPT) Electronic Packaging Technology

作者： Bo Xie Quan Zhou Jingmin Yang School of Mechanical and Electrical Engineering Guangdong University of Technology Guangzhou P.R. China

ISBN: (数字)9798350353808

ISBN: (纸本)9798350353815

This study addresses the challenge of optimizing PID controller parameters in the linear motor drive system of wire bonding machines' XY motion platforms. An innovative approach is proposed, leveraging an Enhanced whale optimization Algorithm (IWOA) to enhance optimization efficiency, precision, and stability. By integrating adaptive weight strategies and random differential mutation tactics, the IWOA dynamically balances global search and local refinement, significantly improving optimization outcomes. The investigation begins with establishing a comprehensive mathematical model of the linear motor, followed by extensive simulations conducted using MATLAB. Results demonstrate that the optimized IWOA outperforms classical Particle Swarm optimization (PSO), Genetic algorithms (GA), and standard WOA in PID parameter tuning, showcasing superior convergence properties. To further enhance system performance, a feedback-feedforward hybrid control system is introduced to minimize tracking errors in system response. Empirical validation confirms the effectiveness of this approach in improving trajectory tracking accuracy and positional precision of the platform. Practical implementation within an industrial context, particularly in the control system of a welding machine, underscores the remarkable advantages of IWOA in PID parameter tuning. This research not only enhances optimization outcomes but also significantly elevates the overall operational performance of wire bonding machines' XY motion platforms.

关键词： Motor drives Trajectory tracking Welding Control systems Motors whale optimization algorithms Wire

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optimization study of professional recognition based on big data

Optimization study of professional recognition based on big ...

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Electronics and Devices, Computational Science (ICEDCS), International Conference on

作者： Xue Huang Chongqing Hailian Technical and Vocational College Chongqing China

ISBN: (数字)9798331527624

ISBN: (纸本)9798331527631

With the continuous development and deepening of the Internet, cloud computing, SG and other information technologies, big data has gradually penetrated into every aspect of all industries, and colleges and universities, as an information-intensive service industry, are the key areas for the application of big data technology. Under the background of big data, colleges and universities integrate traditional business with big data technology to provide differentiated and accurate statistics. In this paper, an optimization study on the survey of professional recognition is conducted by using big data technology. A multi-strategy improved hybrid whale optimization algorithm and ant colony optimization algorithm are proposed and used to optimize support vector regression and random forest model parameters. The construction of the optimization algorithm found that building a channel system of big data can improve the professional recognition of colleges and universities, and the use of big data technology improves the core management level. Finally, it is used in the actual data set to verify the effectiveness of the algorithm to optimize the model parameters.

关键词： Surveys Support vector machines Industries Ant colony optimization Scientific computing Big Data whale optimization algorithms Data models Information technology optimization

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optimization of Search and Rescue Paths for Submersibles Using Monte Carlo Simulations and whale optimization Algorithm

Optimization of Search and Rescue Paths for Submersibles Usi...

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Electronics and Devices, Computational Science (ICEDCS), International Conference on

作者： Mingzhuo Wang Haotian Xu Yuhao Zhang Feiyang Wang Guangxiao Zhang College of Transportation Engineering Dalian Maritime University Dalian China Information Science and Technology College Dalian Maritime University Dalian China Navigation College Dalian Maritime University Dalian China College of International Union Dalian Maritime University Dalian China

ISBN: (数字)9798331527624

ISBN: (纸本)9798331527631

This paper addresses optimizing search and rescue (SAR) paths for submersibles using predictive models. The study first analyzes the forces acting on submersibles in varying conditions of temperature, salinity, and depth, establishing differential dynamic equations through Monte Carlo simulations to predict the motion trajectory with high probability. Comparison between single predictions and average motion trajectories shows that the likelihood of the average trajectory being within 200 meters of any single trajectory in three-dimensional space is less than 92.05%. The study evaluates the effectiveness of various underwater search equipment using the EWM-TOPSIS model, identifying the sonar system, automatic underwater robot, and multibeam underwater terrain detection system as critical components for enhancing SAR capabilities. Furthermore, the research simplifies SAR preparations by focusing on passive search strategies and optimizing search paths using the whale optimization Algorithm (WOA). By migrating and adjusting the model to different sea conditions, the study enhances prediction accuracy and SAR efficiency. The findings underscore the importance of combining probabilistic modeling with advanced optimization techniques to improve the reliability and effectiveness of submersible SAR operations.

关键词： Monte Carlo methods Differential equations Predictive models Search problems Prediction algorithms Mathematical models whale optimization algorithms Trajectory Underwater vehicles optimization

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Media and Investor Sentiment Prediction for Stock Returns - A BP Neural Network Model based on whale optimization Algorithm

Media and Investor Sentiment Prediction for Stock Returns - ...

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IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

作者： Zhongyang Yu Yuan Miao Gaofei Zhao Jinyu Zhang School of Finance Nanjing Audit University Nanjing P.R. China School of Accounting Nanjing Audit University Nanjing P.R. China School of Computer Science Nanjing Audit University Nanjing P.R. China

ISBN: (数字)9798350389166

ISBN: (纸本)9798350389173

Currently, behavioral finance theory has incorporated investor psychology into the research framework of securities. Most investors in China's capital market are retail investors, and it is relatively difficult for them to acquire comprehensive stock information and professional financial knowledge. Therefore, they are more likely to be influenced by external public opinion and their own emotions. Investor sentiment plays a significant role in predicting stock returns. This paper is based on text sentiment analysis of investor comments obtained through web crawling and reports about the stock market from various financial newspapers and local media. By using a sentiment dictionary for sentiment analysis, sentiment scores are assigned to the texts, eventually forming two variables: industry sentiment index and individual stock sentiment index, which are used as the basis for predicting the daily return rate of stocks. We select and construct a whale optimization algorithm to optimize the BP neural network. By analyzing the accuracy and practicality of the BP neural network model in predicting stock returns considering both industry sentiment and individual stock sentiment, we found that the BP neural network optimized by the whale algorithm has a high accuracy in stock market prediction. The research result provides approach ideas for predicting the returns of a single stock in a specific industry. It also offers new directions for the application of media texts and stock reviews. Additionally, it expands the scope of reasonable suggestions for investors in stock selection and stock operations.

关键词： Industries Sentiment analysis Analytical models Accuracy Neural networks Predictive models Media Prediction algorithms whale optimization algorithms Stock markets

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optimization of Hybrid Biogas-Solar Energy Systems for Sustainable Power Generation in Mauritania

Optimization of Hybrid Biogas-Solar Energy Systems for Susta...

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IEEE Power India Conference

作者： Sidahmed Sidi Habib Shuichi Torii Golla Suribabu Anorgul Ismoilovnaa Ashirova Dilshodbek Mansurovich Allashkurov Md Ashraful Islam Department of Advanced Mechanical System Kumamoto University Japan Civil Engineering Department SRKR Engineering College (A) Bhimavaram A.P. India Department of General Professional Sciences Mamun University Khiva Uzbekistan Department Of Specialization Socio-Humanities and Exact Sciences Tashkent State University of Economy Uzbekistan Dept of Electrical and Electronic Engineering Green University of Bangladesh

ISBN: (数字)9798350367065

ISBN: (纸本)9798350367072

This paper presents the optimization and performance analysis of hybrid biogas-PV systems for sustainable power generation in Rosso, Mauritania. The study employs HOMER software to model and optimize both on-grid and off-grid configurations, utilizing local data on solar irradiance, biomass availability, and load demand. The most optimal on-grid system configuration includes 801 kW of PV, a 100 kW biogas generator, and a 408 kW converter, achieving a levelized cost of energy (LCOE) of $0.041/kWh and a net present cost (NPC) of $1.89M. The off-grid system, with 177 kW of PV, a 100 kW biogas generator, 111 kWh of battery storage, and a 75.8 kW converter, yields an LCOE of $0.128/kWh and an NPC of $830,896. Advanced optimization algorithms, including the Grey Wolf Optimizer (GWO) and whale optimization Algorithm (WOA), were applied to further refine the results, with slight LCOE and system size improvements. Environmental impact assessments indicate significant reductions in greenhouse gas emissions, particularly with the off-grid system. The results highlight the feasibility and economic potential of hybrid biogas-PV systems in Mauritania and similar regions and emphasize the role of renewable energy in achieving sustainable and reliable power generation.

关键词： Solar irradiance Urban areas Hybrid power systems Biogas whale optimization algorithms Software Generators Software reliability Greenhouse gases optimization

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An Efficient Data Collection using whale optimization Algorithm (WOA) in Wireless Sensor Network

An Efficient Data Collection using Whale Optimization Algori...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： Udhayakumar Pasupathi A. Gopi Saminathan A. Manikandan M. Subhashini C. Gowrishankar T. S. Poornappriya Department of Computer Science Srimad Andavan Arts and Science College (Autonomous) (Affiliated to Bharathidasan University Tiruchirappalli) Tamilnadu India NPR College of Engineering and Technology Dindigul ECE SRM Institute of Science and Technology Chennai Department of Computer Science Srimad Andavan Arts and Science College (Autonomous) Trichy-5 Department of Electrical and Electronics Engineering Kongunadu College of Engineering and Technology Tiruchirappalli Module Lead Data and Analytics Aspire Systems Pvt Ltd Karnataka

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Wireless sensor networks (WSNs) are an innovative technology that can be used in critical situations such as battlefields as well as commercial applications including buildings, traffic monitoring, smart homes, habitat monitoring, and more. A wireless sensor network (WSN) comprises small nodes capable of wireless sensing, processing, and communication. One of the most pressing concerns plaguing WSNs today is security. This paper suggests and investigates an energy-efficient cluster head (CH) selection approach using the whale optimization Algorithm (WOA) (called WOA-Clustering (WOA-C)) and the Geographic and Energy Aware Routing Algorithm (GEAR). As a result, the suggested method simplifies the decision-making process of energy-aware CHs by utilizing the fitness function that assesses node residual energy and the total energy of neighbouring nodes. Compared to the GWO-C, GABEEC, and PSOMSB protocols, the proposed WOA-GEAR protocol consumes 28%, 22%, and 7.5% less power. The suggested algorithm's network lifetime, power efficiency, buffer occupancy, throughput, and end-to-end latency were evaluated.

关键词： Wireless communication Wireless sensor networks Gears Query processing Clustering algorithms Energy efficiency whale optimization algorithms Routing protocols Security Monitoring

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Intelligent Prediction and Training optimization of Sports using Enhanced whale Optimized Artificial Neural Network

Intelligent Prediction and Training Optimization of Sports u...

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Intelligent algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Hua Wang Ningxia Institute of Technology Shizuishan China

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Enhancing the efficiency of the sports training should be provided since many of the athletic training methods used today are ineffective at considering the unique qualities of individual athletes. The proposed research aimed at developing intelligent prediction and optimization of basketball training using data from Federation Internationale de Basketball (FIBA) website and pre-processed using min max normalization. The Capsule Network (CapsNet) of Convolutional Neural Network (CNN) is used to extract features and the classified using Attention mechanism combined Convolutional Random Forest (AttConv-RF). CapsNet makes it possible to improve the model's capabilities due to the updated loss function. The ConvRF unit recognizes spatial and temporal relationships of the training data, decreasing overfitting issues primarily while training data are limited. The performance prediction is carried out using Enhanced whale optimization Algorithm based Artificial Neural Network (EWOA-ANN). The results showed that proposed EWOA-ANN approach achieved better performance prediction at accuracy with 99.56%, precision of 97.42%, and recall 96.36% when compared with the existing methods Back Propagation Neural Network (BPNN) and eXtreme Gradient Boost (XGB).

关键词： Training Accuracy Training data Feature extraction Prediction algorithms whale optimization algorithms Convolutional neural networks optimization Tuning Sports

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