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检索条件"主题词=whale optimization algorithms"

共 386 条记录，以下是351-360 订阅

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An Improved whale optimization Algorithm and Its Application on Inverse Scattering Problem

An Improved Whale Optimization Algorithm and Its Application...

引用

International Symposium on Antennas, Propagation and EM Theory (ISAPE)

作者： Meng Wang Guizhen Lu Communication University of China School of Information and Communication Engineering Beijing China

ISBN: (纸本)9781665418133

whale optimization Algorithm (WOA) is a stochastic optimization algorithm which imitates the predation of whale for searching their preies. Due to its search efficiency, WOA has been broadly utilized to solve sundry kinds of engineering problems. However, the traditional WOA is often trapped into local optima while solving highly ill-posed inverse problems. Therefore, a new improved WOA is proposed by modifying the original searching path. According to the different search mechanisms of the algorithm, the search path is combined and optimized in order to get better constrain for the search direction and enable the search towards the global optimum. One reconstruction of scatterer permittivity test is given to prove the efficiency of improved WOA. As expected, the proposed IWOA achieves better solutions compared with traditional algorithms.

关键词： Microwave technology Inverse problems whales Search problems whale optimization algorithms Permittivity optimization

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Differential whale optimization Algorithm

Differential Whale Optimization Algorithm

引用

IEEE International Conference on Networking, Sensing and Control

作者： Tao Zheng Baohang Zhang Haichuan Yang Jiayi Li Shangce Gao Faculty of Engineering University of Toyama Toyama Japan

ISBN: (纸本)9781665440493

The whale optimization algorithm (WOA) is a natural-inspired effective optimization algorithm by imitating the behavior of whales rounding up their prey. Due to the high capacity of WOA in terms of exploitation, it is likely to fall into a local optimum as individuals of WOA lack communication. In this paper, we innovatively enlarge the search range of WOA by performing a differential search for each individual in the population, thus enabling the proposed differential whale optimization algorithm (DWOA) to possess an ability of jumping out of the local optimum and meanwhile accelerating its convergence speed. Experimental results based on IEEE CEC2017 benchmark functions demonstrate the superiority of DWOA in terms of solution quality, population diversity, and convergence speed.

关键词： Conferences Sociology Neural networks whales Search problems whale optimization algorithms Sensors

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An Enhanced Lévy-Based whale optimization Algorithm for Global optimization

An Enhanced Lévy-Based Whale Optimization Algorithm for Glo...

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Chinese Automation Congress (CAC)

作者： Jinyu Wu Zhifu Li Ge Ma Jiarong Du Ming Wang School of Mechanical and Electrical Engineering Guangzhou University Guangzhou China

ISBN: (数字)9781665426473

ISBN: (纸本)9781665426480

whale optimization algorithm(WOA) is often used to solve engineering optimization and real parameter optimization problems. However, the original WOA algorithm has some problems. First, The position iteration mechanism based on the optimal whale makes the algorithm lack of exploration ability. In addition, due to the lack of mechanisms to guide the algorithm to escape from the local optimum, the algorithm is prone to stall prematurely. To solve these problems, an Enhanced Lévy-Based whale optimization Algorithm(ELWOA) is proposed in this paper, which uses the Lévy flight mechanism to enhance the in-depth exploration capability of the algorithm and introduces a population diversity maintenance strategy to guide the population to escape from the local optimum. Finally, its performance is verified by the CEC2017 test suite, and the results show that it has a strong advantage in the convergence of the error and convergence speed than the other three algorithms.

关键词： Automation Sociology whales Maintenance engineering whale optimization algorithms Statistics optimization

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Multi-Objective whale optimization for Network Interface Selection in HWNs

Multi-Objective Whale Optimization for Network Interface Sel...

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IEEE International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises (WET ICE)

作者： Brahim Mefgouda Hanen Idoudi National School of Computer Science University of ManoubaTunisia

ISBN: (纸本)9781665427906

The network interface selection (NIS) is an important function that should be executed to connect the user's equipment to the best available network anytime and anywhere by meeting the user/application QoS requirements. Multi-attribute decision-making approaches (MADM) are commonly applied to model and solve NIS problems. Although they can rank networks quickly with a high precision, they suffer from two drawbacks. Firstl, the rank reversal problem (RRP). Second, The selection of the highest-ranking score network without considering user and/or service requirements. In this paper, we introduce a novel approach to solve the above-cited drawbacks. We model the weighting techniques as a multi-objective problem. Then, we apply an extended version of the whale optimization algorithm to obtain the decision criteria weights according to the traffic classes and the chosen MADM approaches. The obtained simulations clearly show that applying our approach with MADM approaches reduces the RRP and satisfies the user/service requirements.

关键词： Wireless communication 5G mobile communication Conferences Surface acoustic waves Collaboration Quality of service whale optimization algorithms

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Power-Efficient Cluster Head Selection in Wireless Sensor Networks using whale and Seagull algorithms

Power-Efficient Cluster Head Selection in Wireless Sensor Ne...

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International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： P.Vinoth Kumar K. Venkatesh Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9781665462761

WSN includes deployed sensors. These small sensor nodes have energy limits since their batteries can't be replaced. Collect, transmit, and transfer data to the base station. Data transmission depletes WSN energy. Clustering is an efficient WSN data transport technique. whale optimization (WSOA) combines a seagull method with a global issue to meet global optimization concerns. The suggested technique helps choose based on residual node energy and nearby nodes, energy-aware cluster heads energy into consideration. Lifetime, energy economy, throughput, and stability of the recommended algorithm are tested. The hybrid metaheuristic's performance is compared to LEACH. The suggested approach outperforms others in residual energy, network longevity, and stability period.

关键词： Wireless sensor networks Metaheuristics Clustering algorithms whales Throughput Control systems whale optimization algorithms

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Optimal Allocation and Size of FACTS Devices Using Several Optimizers: a Comprehensive Study

Optimal Allocation and Size of FACTS Devices Using Several O...

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International Middle-East Power System Conference, MEPCON

作者： Ayman Awad Salah Kamel Mohamed H. Hassan Abdalla A. Ibrahim Francisco Jurado Dept. of Electrical Engineering Faculty of Engineering Aswan University Aswan Egypt Dept. of Electrical Engineering Aswan University Egypt Dept. of Electrical Engineering University of Jaen Jaen Spain

ISBN: (纸本)9781665463645

FACTS devices are playing an essential role in modern power systems, as they have the ability to control the parameters of a system's transmission lines so that the transferability of the system is improved, and the overall performance is enhanced. However, it is crucial to identify the best allocation and size of the incorporated FACTS device to achieve the best results considering the objective required. In this paper, several FACTS devices are incorporated into the IEEE 30-bus standard system, where the optimal allocation and size of such devices are required regarding the cost of generation and power losses as a single objective function of each, considering the integration of wind turbines in the system. The optimization is performed using several recent optimizers including Moth Flame Optimizer (MFO), whales optimization Algorithm (WOA), Skill optimization Algorithm (SOA), and Equilibrium Optimizer (EO), investigating the best results concluded by such optimizers. Simulation results reveal that the EO algorithm is more efficient and superior for optimal allocation and size of FACTS devices solution compared with the other recent algorithms.

关键词： Performance evaluation Thyristors Costs Static VAr compensators Linear programming whale optimization algorithms Wind turbines

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An Efficient Neurocontroller Position Method for PMSM Drive System

An Efficient Neurocontroller Position Method for PMSM Drive ...

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IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)

作者： O. Aguilar-Mejia H. Minor Popocatl J.M. Garcia-Morales C. O. Castillo-Ibarra A. Valderrabano-Gonzalez UPAEP University Puebla Puebla Mexico Facultad de Ingenieria Universidad Panamericana Zapopan Jalisco Mexico

ISBN: (纸本)9781665458931

PMSM has been widely used in high-precision variable-speed applications, however, the control scheme demands normally a high dynamic performance under several operating contidions. Due to the non-linear nature of the PMSM, the use of an adaptive controller based on B-spline neural networks is proposed to determine the control signals. The proposed control technique through neural networks exhibits the best performance because it can be adapted to each operating condition, demanding low computational cost for an online operation, and considering non-linearities of the system. The performance of the proposed controller is evaluated in the presence of uncertainties. The results are compared with the conventional PI controller, optimized using whale optimization algorithm.

关键词： Neurocontrollers Uncertainty PI control Adaptive systems Neural networks whale optimization algorithms Power electronics

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An Optimized Job Scheduling Mechanism for Mapreduce Framework Using DIW-WOA in Big Data

An Optimized Job Scheduling Mechanism for Mapreduce Framewor...

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Knowledge Engineering and Communication Systems (ICKECS), International Conference on

作者： Vishal Kumar Sumit Kushwaha University Institute of Computing Chandigarh University India

For processing, storing, and managing huge amounts of data, big data has turned out to be famous. The most popular map reduce programming model is the open-source Hadoop. It executes the MapReduce (MR) programming model to process big data with MR jobs. Nevertheless, scheduling MR jobs across multiple nodes has been regarded as an optimization issue in spite of current endeavors toward ameliorating MR's performance. Hence, to carry out an MR Job scheduling centered on Completion Time (CT) and cost whilst meeting the security constraints, a new optimization algorithm like Decreasing Inertia Weight induced whale optimization Algorithm (DIW-WOA) is proposed. At first, for big data clustering, a Butterfly optimization-centered K-Means clustering Algorithm (BO-KMA) is applied. At last, for scheduling the client's tasks or jobs to the optimized scheduler. The whale optimization algorithm is hybridized with inertia weight concept and finally DIW-WOA is employed. When weighed against the other well-known algorithms, the experimental outcomes exhibit that the proposed algorithms for data clustering, DS and Task Scheduling (TS) achieve better performance in terms of make-span and throughput. It can be incorporated into the Hadoop environment.

关键词： System performance Clustering algorithms Big Data Programming Throughput Scheduling whale optimization algorithms

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Comparative Analysis of Swarm algorithms to Classification of covid19 on X-Rays

Comparative Analysis of Swarm Algorithms to Classification o...

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Data Science and Intelligent Computing (ICDSIC), International Conference on

作者： Abubakr S. Issa Yossra H. Ali Tarik A. Rashid Computer sciences department University of Technology Baghdad Iraq Computer Science & Engineering Department University of Kurdistan Hewler Iraq

The use of Chest radiograph (CXR) images in the examination and monitoring of different lung disorders like infiltration, tuberculosis, pneumonia, atelectasis, and hernia has long been known. The detection of COVID-19 can also be done with CXR images. COVID-19, a virus that results in an infection of the upper respiratory tract and lungs, was initially detected in late 2019 in China's Wuhan province and is considered to majorly damage the airway and, thus, the lungs of people afflicted. From that time, the virus has quickly spread over the world, with the number of mortalities and cases increasing daily. The COVID-19 effects on lung tissue can be monitored via CXR. As a result, This paper provides a comparison regarding k-nearest neighbors (KNN), Support-vector machine (SVM), and Extreme Gradient Boosting (XGboost) classification techniques depending on Harris Hawks optimization algorithm (HHO), Salp swarm optimization algorithm (SSA), whale optimization algorithm (WOA), and Gray wolf optimizer (GWO) utilized in this domain and utilized for feature selection in the presented work. The dataset used in this analysis consists of 9000 2D X-ray images in Poster anterior chest view, which has been categorized by using valid tests into two categories: 5500 images of Normal lungs and 4044 images of COVID-19 patients. All of the image sizes were set to $200\times 200$ pixels. this analysis used several quantitative evaluation metrics like precision, recall, and F1-score.

关键词： COVID-19 Support vector machines Tuberculosis Lung Feature extraction whale optimization algorithms Classification algorithms

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Research on online detection and identification method of vibration signal of cutting process

Research on online detection and identification method of vi...

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Science and Technology Innovation (IAECST), International Academic Exchange Conference on

作者： Youzhi Zhang Xiu Wu Qiang Shao Yu Liu Dalian Minzu University Dalian China

Chattering is one of the major performance limitations during turning, which leads to poorer surface quality, dimensional errors, and shorter tool and machine life, so tool condition monitoring is very important. In this paper, for spindle chatter monitoring in turning process, a whale optimization Algorithm is used to optimize the variational modal decom- position, and a new whale optimization Algorithm-Variational Modal Decomposition-Energy Distribution method is proposed to identify the spindle chatter, and this method is confirmed to improve the identification accuracy by this experiment. And the approximate entropy recognition method and the sample entropy recognition method are tested. The recognition accuracy and computation time of the three methods were compared. The conducted experimental study shows that the proposed whale optimization Algorithm-Variational Modal Decomposition- Energy Distribution method provides a recognition method with higher recognition accuracy, better stability and less computation time for chattering monitoring.

关键词： Vibrations Condition monitoring Technological innovation Approximation algorithms Turning whale optimization algorithms Entropy

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