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检索条件"主题词=Whale optimization algorithms"

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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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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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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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IFSWOA: Integrated Biomarker Selection Method Based on Incremental Feature Selection and whale optimization Algorithm

IFSWOA: Integrated Biomarker Selection Method Based on Incre...

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Computer Information Science and Application Technology (CISAT), International Conference on

作者： Yuanyuan Han College of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

In biological omics research, a notable characteristic of datasets is the overwhelming number of features compared to samples, posing significant challenges for data mining and model development. Feature selection algorithms serve as an effective approach to tackle this issue, aiming to identify the most representative biomarkers and consequently build predictive models with superior performance. However, the complexity and latent patterns within the feature selection solution space render the search for optimal feature subsets a daunting task. The Incremental Feature Selection (IFS) algorithm stands out in the field of feature selection due to its statistical underpinnings and excellent machine learning performance. Notably, different IFS algorithms exhibit complementary search capabilities when exploring feature subsets. This suggests that integrating multiple IFS algorithms can enable a more efficient exploration of the feature space, leading to the discovery of superior feature combinations. Drawing upon this insight, we introduce a novel feature selection method called IFSWOA. This method integrates three IFS-based feature selection algorithms and introduces an enhanced whale optimization Algorithm (WOA) to further refine the selected features. Through this approach, we significantly enhance the quality of the selected feature subsets and the predictive performance of the models. Experimental results demonstrate that the IFSWOA algorithm outperforms existing feature selection algorithms on a majority of omics datasets. This achievement not only validates the efficacy of IFS algorithms in biological omics feature selection, but also provides a novel and efficient feature selection strategy, opening new avenues for the analysis and mining of biological omics data.

关键词： Machine learning algorithms Biological system modeling Machine learning Predictive models Biomarkers Feature extraction Prediction algorithms whale optimization algorithms Data mining optimization

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Optimal Allocation of Multiple Distributed Generation Using an Enhanced whale optimization Algorithm

Optimal Allocation of Multiple Distributed Generation Using ...

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IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)

作者： John Laurence P. Elambayo Adonis Emmanuel D C. Tio Electrical and Electronics Engineering Institute University of the Philippines Diliman Quezon City Philippines

ISBN: (数字)9798350373929

ISBN: (纸本)9798350373936

The integration of distributed generation (DG) into the distribution system is a complex problem that requires an efficient optimization technique. To maximize benefits while minimizing disadvantages, it is crucial to determine optimal locations and capacities for DG installations. Despite the frequent use of recently developed optimization algorithms, their enhanced counterparts are rarely investigated. Moreover, most research relied on peak loads, which do not accurately reflect real-world demand. We proposed using the enhanced whale optimization algorithm (E-WOA) for optimal DG allocation in a radial distribution system, aiming to minimize power and energy losses. This study also developed representative variable load profiles based on real-world consumption data. The backward/forward sweep algorithm is used for load flow analysis, with the IEEE 33-Bus radial distribution system serving as the test system. The optimization was simulated over 100 independent runs, each with 200 iterations and a population of 50. Results demonstrated that E-WOA effectively solves the optimal DG allocation problem, achieving a power loss of 72.785 kW for three DGs at unity power factor, outperforming the standard WOA and several recent algorithms, and matching the performance of hybrid algorithms. Comparison with WOA across different loading scenarios showed that E-WOA consistently produced higher quality solutions at the cost of slower convergence speeds, longer runtimes, and poor scalability. These suggest E-WOA’s limitations in addressing large-scale optimization tasks.

关键词： Runtime Scalability Asia whale optimization algorithms Hybrid power systems Distributed power generation Resource management optimization Standards Convergence

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Trade-Off Planning of Electric Vehicle Charging Stations Based on Improved whale optimization Algorithm

Trade-Off Planning of Electric Vehicle Charging Stations Bas...

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New Energy and Electrical Technology (ISNEET), International Symposium on

作者： Lei Zheng Zhongpei Liu Zhiyong Zha Huan Xu Hanghan Liang Guangcheng He Information Center State Grid Hubei Information and Telecommunication Company Wuhan Hubei China Electronic Information School Wuhan University Wuhan Hubei China

ISBN: (数字)9798331505905

ISBN: (纸本)9798331505912

The surge in electric vehicles poses new challenges to charging stations, and a reasonable layout plan will promote the development of the electric vehicle industry. In the context of charging demand affected by seasonality, daily variations, we propose a method for the layout of electric vehicle charging stations calculated in granularity of hours. Firstly, based on the demand points after Monte Carlo simulation, we propose a dynamic time-varying charging station planning model with the optimization objectives of maximizing the annual revenue and minimizing user losses, under considering distance constraints, power constraints, queuing time constraints. To address this issue, we propose a two-stage search algorithm that combines random search with whale optimization Algorithm (RS-WOA) to find optimized solutions at both coarse and fine granularity levels. Finally, numerical simulation experiments were conducted in an example of the Optics Valley region in Wuhan, China. The experimental results show that our proposed algorithm achieved an annualized profit of 17.8399 million, which improved 5.3% and 37.7% compared to the RS algorithm and WOA algorithm, respectively.

关键词： Costs Heuristic algorithms Layout Charging stations whale optimization algorithms Electric vehicle charging Planning Time factors Vehicle dynamics optimization

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Depression Diagnosis using Restricted Crossover and Mutation Based whale optimization Algorithm

Depression Diagnosis using Restricted Crossover and Mutation...

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Computing, Sciences and Communications (ICCSC), International Conference on

作者： Prableen Kaur Ritu Gautam Manik Sharma Dept. of Computer Sci. And App. GMN College Ambala India Dept. of Computer Sci. And App. Amity University Noida India Dept. of Computer Sci. And App. DAV University Jalandhar India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

whale optimization algorithm (WOA) and Genetic Algorithm (GA) are very effective soft computing techniques. Numerous soft computing methods, such as evolutionary and swarm intelligence, have been used to identify the best features needed for the diagnosis of psychiatric diseases, such as depression, anxiety, and stress. A novel hybrid feature selection method called RCM-WOA, which is based on the principles of genetic algorithm with some restriction on crossover and mutation and whale optimization technique has been proposed as a solution to the feature selection problem. In addition, the outcomes of the suggested approach are contrasted with nine distinct soft computing methodologies.

关键词： Accuracy Sensitivity Error analysis Anxiety disorders Feature extraction Depression whale optimization algorithms Data mining Genetic algorithms Dementia

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optimization of cloud computing task scheduling based on whale optimization algorithm with quasi-opposition-based and nonlinear factor

Optimization of cloud computing task scheduling based on wha...

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Internet of Things, Automation and Artificial Intelligence (IoTAAI), International Conference on

作者： Xiong Xuan Shaobo Li Liya Yu Fengbin Wu Xingxing Zhang Xue An State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China State Key Laboratory of Public Big Data Guizhou University Guiyang China College of Mechanical Engineering Guizhou University Guiyang China

ISBN: (数字)9798350386974

ISBN: (纸本)9798350386981

Task scheduling has a significant impact on the resource availability and operation cost of the system in cloud computing. In order to improve the efficiency of task execution in the cloud for a given computational resource, the improved whale optimization algorithm is applied to the multi-objective optimization model for cloud task scheduling. Specifically, the quasi-opposition-based learning strategy is added in the initiation phase to increase the diversity of the population. Meanwhile, a novel nonlinear convergence factor was introduced to augment the local search capability. Experimental results indicate that the improved whale optimization algorithm converges faster and searches with higher precision in finding the optimal task scheduling solution compared to existing meta-heuristic algorithms. In addition, the modified algorithm achieves superior system resource usage when dealing with both small and large tasks.

关键词： Cloud computing Costs Processor scheduling System performance optimization models Metaheuristics Scheduling whale optimization algorithms Internet of Things Convergence

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