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检索条件"主题词=Whale Optimization Algorithms"

共 402 条记录，以下是171-180 订阅

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Enhancing Arrhythmia Disease Classification Using MLP with whale optimization Approach

Enhancing Arrhythmia Disease Classification Using MLP with W...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： R. Mythili A.S. Aneetha Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Cardiac arrhythmias are a serious health danger. Thus, timely diagnosis and treatment depend on precise and effective categorization techniques. The paper addresses the implementation of Multilayer Perceptron (MLP) in conjunction with whale optimization-based feature selection, Particle Swarm optimization (PSO), and Antlion Optimizer (ALO) for the categorization of Atrial Fibrillation (AFib) illnesses. Key performance indicators, including Accuracy, precision, and recall, will be examined to determine whether MLP using whale optimization for feature selection works better than conventional techniques. The results indicate that the suggested strategy dramatically improves the classification performance compared to established methods based on thorough experimentation and comparative analysis. The combination of MLP and whale optimization-based feature selection exhibits excellent recall, Accuracy, and precision and is a viable option for reliable AFib illness categorization. From the results obtained, the proposed MLP+WO gave an Accuracy of 90.74 % and precision and recall of 0.90 and 0.88, respectively. The language used is Python.

关键词： Accuracy Arrhythmia Neural networks optimization methods Multilayer perceptrons Feature extraction whale optimization algorithms

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Research on Integrated Energy System Planning Based on the whale optimization Algorithm

Research on Integrated Energy System Planning Based on the W...

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Energy, Power and Grid (ICEPG), International Conference on

作者： Xu Qian Xiaochen Xu Jingdong Zhu Pengxu Dong Yibo Li Yongli Wang Electromechanical Engineering Institute PowerChina Huadong Engineering Corporation Limited Zhejiang China School of Economics and Management North China Electric Power University Beijing China

ISBN: (数字)9798350377798

ISBN: (纸本)9798350377804

In this paper, a comprehensive energy system planning optimization method based on heuristic algorithm is proposed, which aims to improve the economic efficiency of the system. Firstly, the background and development status of the integrated energy system are analyzed, and the challenges and optimization opportunities of equipment configuration and modeling are discussed. Subsequently, an optimization method based on economic performance was introduced, and the whale optimization algorithm was used for system optimization. Through case analysis, the results show that the proposed method can significantly reduce the operating cost of the system. Under the initial population size of 120 iterations, the whale optimization algorithm converged around the 44th generation, and the final solution obtained was ${\$}$6,218,367.29. The results obtained by the genetic algorithm are $1.90 \%$ higher than those obtained using the whale optimization algorithm. Therefore, the algorithm used in this paper shows strong global search ability and improves the optimization accuracy.

关键词： Analytical models Adaptation models Uncertainty Heuristic algorithms optimization methods Dynamic scheduling whale optimization algorithms Planning Load modeling Genetic algorithms

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Hybrid Approach Using LSTM Neural Networks and whale optimization Algorithm For Enhancing Depression Diagnosis

Hybrid Approach Using LSTM Neural Networks and Whale Optimiz...

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Future Telecommunications and Artificial Intelligence (IC-FTAI), International Conference on

作者： Alwan Atta Dina El Sayad Doaa Ezzat Safaa Amin Mahmoud El Gamal Scientific Computing Department Faculty of Computers and Information Sciences Ain Shams University & Galala University Scientific Computing Department Faculty of Computers and Information Sciences Ain Shams University

ISBN: (数字)9798331534080

ISBN: (纸本)9798331534097

Depression is a prevalent mental disorder, and accurate diagnosis is essential for effective intervention by proposing a novel approach for diagnosing depression using Long Short-Term Memory (LSTM) neural networks combined with swarm intelligence algorithms. Accordingly, this research pays major attention to how the strength of LSTM in sequential data and optimization performance through swarm intelligence enhance the accuracy and reliability of the diagnosis of depression. So far, we utilized the whale optimization Algorithm (WOA) algorithm for the optimal tuning of the model parameters of LSTM toward their optimal performance. The experimental results confirm the effectiveness of our approach, where the proposed model has an RMSE value of 3.75 and an MAE value of 3.15. These results indicate a higher performance compared to state-of-the-art methods and offer great potential for LSTMs combined with the WOA algorithm in the accurate diagnosis of depression. This may be a signal that the hybrid approach will serve perfectly in a clinical setting by allowing clinicians to identify depression among patients more precisely and reliably.

关键词： Accuracy Neural networks Depression whale optimization algorithms Telecommunications Telecommunication network reliability Particle swarm optimization Long short term memory Tuning optimization

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Space Crowdsourcing Allocation Scheme Based On Improved Fishing optimization Algorithm

Space Crowdsourcing Allocation Scheme Based On Improved Fish...

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Computer, Vision and Intelligent Technology (ICCVIT), International Conference on

作者： Xingyu Zhou Minglan Fu Xu Zhang School of Computer Science and Technology Huaibei Normal University Huaibei China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

This study proposes a mobile crowdsourcing task allocation method based on the Improved Fishing optimization Algorithm (ICFOA-SCAM), aiming to address the issue of user preferences and waiting time being overlooked in existing crowdsourcing platforms, thereby improving user satisfaction. Optimizing task allocation has become the key to improving service quality. The ICFOA-SCAM method comprehensively considers the personalized preferences of users, the abilities of workers, and the spatiotemporal characteristics of tasks. By defining key concepts such as spatiotemporal units, user preference evaluation scores, waiting time evaluation scores, and user satisfaction evaluation scores, it enriches the definition of tasks and crowdsourcing authors, enabling the model to more comprehensively consider the complexity of spatial crowdsourcing task allocation. The experimental results show that the ICFOA-SCAM method outperforms traditional white whale optimization algorithm and grey wolf optimization algorithm in terms of user satisfaction and task allocation efficiency. ICFOA-SCAM improves the global search capability and convergence speed of the algorithm by introducing Singer chaotic mapping initialization and Lévy flight strategy. The contribution of this article lies in proposing a new crowdsourcing task allocation model, which not only improves the efficiency and quality of task allocation, but also enhances user satisfaction. Future research will explore how to leverage workers' social relationships to further enhance the effectiveness of task allocation, as well as improve the service quality and user satisfaction of crowdsourcing workers while protecting location and information privacy.

关键词： Crowdsourcing Social networking (online) Soft sensors whale optimization algorithms Stability analysis Spatiotemporal phenomena Resource management User preference optimization Convergence

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Research on Capacity optimization Configuration of Light Storage Charging DC Microgrid

Research on Capacity Optimization Configuration of Light Sto...

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2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Lei Fu Zicheng Li School of Electrical Information Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

Due to the high investment cost and insufficient system stability of the photovoltaic charging DC microgrid, a capacity optimization model is constructed with the objective functions of annual net cost, which represents economic benefits, and power deviation rate, which represents system stability. Aiming at the problem of insufficient search ability and low optimization accuracy in solving optimization configuration models with non-zero solutions using traditional whale optimization algorithms, a multi strategy improved whale optimization algorithm is proposed, which integrates quasi adversarial learning mechanism, adaptive weights, nonlinear convergence factor, Gaussian difference mutation, and greedy selection. Finally, a case study was conducted on a residential area in Wuhan, and the results showed the feasibility of the model and the effectiveness of the improved algorithm.

关键词： Photovoltaic systems Adaptation models Costs Biological system modeling Microgrids whale optimization algorithms Stability analysis optimization Convergence Load modeling

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Tourist Route Recommender System Using whale optimization Algorithm (A Case Study of Yogyakarta)

Tourist Route Recommender System Using Whale Optimization Al...

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Technology Innovation and Its Applications (ICTIIA), International Conference on

作者： Vivi Fitriani Z. K. A. Baizal School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9798350351613

ISBN: (纸本)9798350351620

Yogyakarta in Indonesia is a top tourist destination known for its diverse and renowned attractions. Many tourists plan multi-day trips to explore the area. Tourists usually depend on travel agents, but many travel route recommender systems only focus on planning one-day trips and need to consider factors like cost, duration, and destination rating. Therefore, this paper presents a multi-day travel route recommender system that considers user preferences such as cost, time duration, and destination ratings. We solve the route optimization problem analogous to finding a solution to the Traveling Salesman Problem (TSP). We use the whale optimization Algorithm (WOA) and Multi-Attribute Utility Theory (MAUT) to recommend the best route based on user preferences. Test results show that the WOA algorithm performs better than Simulated Annealing (SA) regarding the maximum number of days for each POI (Point of Interest) visited, fitness value, and execution time.

关键词： Technological innovation Costs Utility theory Simulated annealing Traveling salesman problems whale optimization algorithms Planning Recommender systems optimization

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Fault Diagnosis of Inter-turn Short Circuit Fault in Star-delta Winding Structure Based on Parameter optimization VMD and Fuzzy Entropy

Fault Diagnosis of Inter-turn Short Circuit Fault in Star-de...

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IEEE Asia Power and Energy Engineering Conference (APEEC)

作者： Zhenfei Chen Miao Bao Zhihao Ling Mohamed Elhind Zhixin Li School of Electrical and Power Engineering Hohai University Nanjing China Marketing Service Center Jiangsu Electric Power Co. Ltd. Nanjing China

ISBN: (数字)9798350373479

ISBN: (纸本)9798350373486

A star-delta connection winding structure can restrain the low order harmonic of armature magnetic field and improve the operating efficiency of machine. However, the risk of inter turn short circuit in its structure is higher. In this paper, the short-circuit current of inter-turn short circuit occurring at different winding positions is derived. An adaptive weight strategy is used to improve whale optimization algorithm (WOA). The improved WOA is adopted for optimizing the parameters of variational mode decomposition (VMD). VMD based on improved WOA for processing current signal is proposed. Finally, by calculating the fuzzy entropy of current and torque, a fault diagnosis method for inter-turn short circuit fault in star-delta connection winding structure is proposed. The accuracy of this fault diagnosis method can reach over 90%. And it can effectively distinguish that the inter-turn short circuit fault occurs in Y connection part or △ connection part.

关键词： Fault diagnosis Torque Short-circuit currents Windings Feature extraction Entropy whale optimization algorithms

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Refraction-Learning Based whale optimization Algorithm with Opposition-Learning and Adaptive Parameter optimization

Refraction-Learning Based Whale Optimization Algorithm with ...

引用

Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Aditya Anand Lakshay Rastogi Ansh Agarwaal Shashank Bhardwaj Netaji Subhas University of Technology New Delhi India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

In this paper, the development of an enhanced variant of the whale optimization Algorithm (WOA) is proposed which addresses some of the limitations, namely slow convergence speed, a tendency to get stuck in local optima, and loss of population diversity. The proposed variant integrates novel strategies namely Refraction Learning (RL) and Opposition-Based Learning (OBL) along with an adaptive parameter optimization mechanism. Owing to the unique amalgamation of the above-mentioned strategies, the performance of the conventional WOA is augmented by intensifying the search process and smoothing it overall. This leads to superior performance in the case of multi-modal landscapes, escaping local optima, and a more eclectic investigation of the search space. Through comprehensive benchmark testing across 23 functions, demonstration occurs that the algorithm outperforms the original WOA and other modern optimization techniques. The proposed WOA variant shows significant improvements in terms of convergence rate, global search ability, and solution accuracy. The empirical results highlight the strengths of the approach and indicate its potential as an effective optimization tool for solving complex real-world problems.

关键词： Smoothing methods Sociology Circuits Benchmark testing whale optimization algorithms Task analysis Statistics

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Symmetric Adaptive Population Division Based Salp Swarm Algorithm for Resource optimization in Cloud Computing

Symmetric Adaptive Population Division Based Salp Swarm Algo...

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

作者： Sidagouda Basagouda Patil Mukund A. Kulkarni Computer Science (Management Studies) Bharati Vidyapeeth (Deemed to be University) Kolhapur India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

In the present advanced world, Cloud Computing (CC) is essential across various applications because of its quick development and wide utilization of Internet technologies. However, it faces some issues in the identification of best resources and task allocation for classifying relevant tasks based on the load. The Symmetric Adaptive Population Division based Salp Swarm Algorithm (SAPD-SSA) is proposed for optimal resource allocation in the CC environment. The SAPD-SSA enhances the convergence speed by efficiently dividing populations into subgroups. This approach enhances the balance among exploration and exploitation which leads to better resource utilization in CC. Moreover, it effectively handles complex and dynamic cloud environments which ensures optimal resource allocation with less computational cost. The multi-objective functions of makespan, resource utilization and computation cost are considered in this research for estimating the effectiveness of proposed SAPD-SSA. The makespan, resource utilization and computation cost are taken as evaluation metrics for assessing SAPD-SSA performance. The SAPD-SSA attains a makespan of 250, resource utilization of 98.28% and computation cost of 950$ for the task of 200, which is superior to the Hybrid whale optimization algorithm-with Mutation based Bees algorithm (HWOA-mBA).

关键词： Measurement Cloud computing Costs Heuristic algorithms Metaheuristics Dynamic scheduling whale optimization algorithms Computational efficiency Resource management Convergence

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Improved whale optimization Algorithm and Optimized Long Short-Term Memory for DDoS Cyber Security Threat

Improved Whale Optimization Algorithm and Optimized Long Sho...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Sanjaikanth E Vadakkethil Kiran Polimetla Srikanth Velpula Piyush Kumar Pareek Dipti Sontakke Somanathan Pillai School of Electrical Engineering and Computer Science University of North Dakota Grand Forks USA Dept. of Information Technology Adobe Inc Austin Texas USA Department of Electrical and Electronics Engineering SR University Warangal India Department of AIML and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India Consultant Capgemini Inc USA

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

The Distributed Denial of Service (DDoS) attacks is a critical cyber security threat that blocks the services for users and leads to important damage for reputation and effective customers. The existing methods provides low classification accuracy due to irrelevant features in classification performance. The Improved whale optimization Algorithm (IWOA) is proposed for the selecting of relevant features which improved the classification performance. The selected features are detected and classified by Optimized Long Short-Term Memory (OLSTM) that provided high detection rate of DDoS attacks. The one-hot encoding and min-max normalization techniques are used in the data pre-processing stage to improve the performance of classification. The CIC-DDoS 2019 dataset are used for evaluating the proposed method. The IWOA-OLSTM method attained highest accuracy 97.12%, precision 96.74%, recall 96.27%. f1-score 96.54% and DR 93.26% on CIC-DDoS 2019 dataset which is effective than Convolutional Neural Network (CNN) - Bidirectional Long Short-Term Memory (BiLSTM) and CNN-LSTM.

关键词： Deep learning Denial-of-service attack Feature extraction whale optimization algorithms Encoding Classification algorithms Convolutional neural networks

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