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检索条件"主题词=Whale optimization algorithms"

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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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Predictive Hybrid Routing with Multi Objective optimization Model for Opportunistic Routing for VANET

Predictive Hybrid Routing with Multi Objective Optimization ...

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International Conference on Engineering Technology and their Applications (IICETA)

作者： Ali Hashim Abbas Ahmed Jamal Ahmed Sami Abduljabbar Rashid Nejood Faisal Abdulsattar Mustafa Hamid Hassan Mohammed Ahmed Jubair Mohammed I. Habelalmateen Department of Computer Technical engineering College of Information Technology Imam Ja’afar Al-Sadiq University Al-Muthanna Iraq Department of Computer Engineering Techniques Al-Maarif University College Al-Anbar Iraq Department of Computer Technical Engineering College of Technical Engineering Islamic University Najaf Iraq

ISBN: (数字)9781665472159

ISBN: (纸本)9781665472166

Opportunistic network model is one among the prediction-based routing approach for effective transmission of data. In opportunistic network identification of routing path are critical task due to mobility and dynamic nature of network. In microscopic opportunistic routing approach, route prediction is an essential factor for effective communication. In addition to that decision making is also plays an important role in computation of routes. In order to overcome this entire problem we introduced a novel mechanism namely Fuzzy Set Theory with whale optimization Algorithm (FST-WOA). The first objective is to develop appropriate machine learning based that incorporates fuzzy set theory for prediction of routes in the network based on the distance, number of hop and energy level of nodes. ALEXNET architectural model is used for route prediction in fuzzy set theory. This fuzzy based ALEXNET model integrated with multi-objective optimization technique called whale optimization Algorithm (WOA). For the process of simulation Network Simulator (NS3) is used. The proposed method is tested and compared with the earlier models such as BOR and RLFGRP. The parameters used for the process of performance analysis are, latency calculation, Packet Delivery Ratio and routing overhead.

关键词： Simulation Vehicular ad hoc networks Predictive models Routing Prediction algorithms Fuzzy set theory whale optimization algorithms

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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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A Many-objective Chaotic whale optimization based Automatic Clustering Algorithm for Distributed Data Analysis in WSN

A Many-objective Chaotic Whale Optimization based Automatic ...

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Electrical Power Engineering, Communication and Computing Technology(ODICON), Odisha International Conference on

作者： Dinesh Kumar Kotary Satyasai Jagannath Nanda Dept. of Electronics and Communication Engg. Institute of Engineering Technology Lucknow Uttar Pradesh India Dept. of Electronics and Communication Engg. Malaviya National Institute of Technology Jaipur Rajasthan India

ISBN: (纸本)9781665478403

The potential applications of Wireless senor networks (WSN) has increased rapidly in weather reporting, target tracking, oxygen content monitoring and many other applications due to their low cost and less power requirements. The sensor nodes in WSN collect the dataset which is transferred to base station for further processing. Clustering is a key part of processing WSN dataset. To reduce the burden of base station researchers are using distributed approaches for clustering where individual sensor node performs cluster analysis with collaboration of neighboring nodes. Nature inspired algorithms provides better clustering results for WSN dataset in single and multi-objective framework. As the complexity of WSN data set increases the single and multi objective algorithms fails to give correct partitions when applied for clustering. To alleviate this problem this paper introduces an automatic distributed many-objective chaotic whale optimization algorithm (ADCWOA) to perform distributed clustering in WSN datasets. The proposed ADCWOA outperforms automatic distributed many objective particle swarm optimization (ADPSO) and automatic distributed K-Means (ADK-Means).

关键词： Wireless communication Wireless sensor networks Base stations Target tracking Chaotic communication Clustering algorithms whale optimization algorithms

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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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