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检索条件"主题词=Whale optimization algorithms"

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Long Short-Term Memory for Short Term Load Forecasting with Singular Spectrum Analysis and whale optimization Algorithm

Long Short-Term Memory for Short Term Load Forecasting with ...

引用

IEEE Asia Power and Energy Engineering Conference (APEEC)

作者： Ruixiang Zhang Meng Yuan Zhaorui Jin Ziyu Zhu Yuanhui Chen Yu Wang Yaojie Sun Longjun Zhao School of Information Science and Technology Fudan Univesity Shanghai China Institute for Six-Sector Economy Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Shanghai Engineering Research Center for Artificial Intelligence and Integrated Energy System Shanghai China China Xiongan Group Digial City Technology Co. Ltd. Beijing China

Short-term load forecasting is necessary for the safety and high efficiency of power system. In recent years, a large number of artificial intelligence-based methods have been used for short-term load forecasting by researchers, among which deep learning methods such as long short-term memory (LSTM) has been proved to be better at load forecasting. However, in the process of constructing LSTM models, data processing and hyperparameter selection tend to depend on experience, resulting in relatively slow modeling speed. This study proposes a short-term load forecasting method with singular spectrum analysis (SSA), LSTM network and whale optimization algorithm (WOA). SSA is used to decompose and reorganize the load sequence, and the WOA is used to optimize the hyperparameters of the LSTM network. The results show that the proposed method performs better than the benchmark in the three counties of the United States used for verification and comparison, that is, SSA-WOA-LSTM can be used to improve the accuracy of the short-term load forecasting.

关键词： Load forecasting Predictive models Hyperparameter optimization Data models whale optimization algorithms Forecasting optimization

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whale optimization for Energy Efficient Healthcare System in WBAN Environment

Whale Optimization for Energy Efficient Healthcare System in...

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Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), International Conference on

作者： Ranjana Sharma Rahul Agarwal Department of Computing Science & IT Teerthanker Mahaveer University Moradabad India

ISBN: (纸本)9781665493970

Researchers have lately been interested in intelligent healthcare systems because of its significance in an IoT setting. In these scenarios, biomedical sensors are implanted into patients in order to collect data on their health status throughout a wireless body area network (WBAN). The suggested approach splits the body into an upper, lower, and centre section. A more refined version of the LEACH clustering technique is used in each area. As a further step, we apply an evolutionary technique called the whale optimization algorithm (WOA) to choose out our cluster leaders. For the purpose of assessing the efficacy of the suggested technique, the algorithm is implemented in a Matlab platform and evaluated to the conventional algorithms, which has been suggested before for the same kinds of problems. The suggested system provides longer battery life for devices in a wireless body area network, and it surpassed the alternatives.

关键词： Wireless communication Wireless sensor networks Clustering algorithms Medical services Pressing Body area networks whale optimization algorithms

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Automated Groundnut Leaf Disease Recognition Using whale optimization Algorithm with Deep Learning Model

Automated Groundnut Leaf Disease Recognition Using Whale Opt...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： G. Suresh K. Seetharaman Department of Computer and Information Science Annamalai University Chidambaram Tamil Nadu India

ISBN: (纸本)9781665489638

Groundnut is a significant oilseed crop in the world, and India is the second-largest producer of groundnuts. This crop can be attacked by several diseases that are predominant factors contributed towards the loss of degradation and productivity in the quality; leads to a low agricultural economy. Hence, there is a need to find more reliable and better automation solution to identify groundnut leaf disease. In this study, we propose an Automated Groundnut Leaf Disease Recognition using whale optimization Algorithm with Deep Learning (AGLDR-WOADL) technique. The presented AGLDR-WOADL technique comprises a series of operations. Primarily, threshold based segmentation is applied to identify the diseased portions of the image. Next, NASNet large feature extraction technique is exploited. Finally, the WOA with long short term memory (LSTM) method is utilized to recognize various kinds of plant diseases. To demonstrate the enhanced performance of the AGLDR-WOADL approach, an extensive range of simulations were performed. The comprehensive comparison study indicated the improved outcomes of the AGLDR-WOADL approach over other existing methods with higher accuracy of 99.63%.

关键词： Deep learning Productivity Image segmentation Crops Feature extraction whale optimization algorithms Reliability

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An enhanced whale optimization Algorithm based Control Strategy for LVRT Capability Enhancement of Grid-Connected DFIG-WECS

An enhanced Whale Optimization Algorithm based Control Strat...

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IEEE India Conference (INDICON)

作者： Arindam Chakraborty Tanmoy Maity Deparment of Electrical Engineering Indian Institute of Technology(ISM) Dhanbad Dhanbad Jharkhand India

ISBN: (纸本)9781665452724

A novel application of enhanced whale optimization algorithm (EWOA) is proposed in this research work to improve the low voltage ride through (LVRT) capability of the grid-connected doubly fed induction generator (DFIG) based wind energy conversion system (WECS). The EWOA is proposed in this article due to rapid convergence velocity, simple construction, and low computational burden. The proposed EWOA modifies the gains of all proportional-integral (PI) controllers at a high convergence speed. The optimal PI controllers are designed by the proposed EWOA. There are four PI controllers are employed in the rotor side converter (RSC), and another four PI controllers are applied in the grid side converter (GSC) under the cascaded structure. The DFIG is integrated into the grid through RSC and GSC with a common dc-link capacitor. The RSC is regulated to reduce the active power loss by using cascaded structure-based PI controllers, whereas GSC is regulated to keep constant the dc-link voltage and grid side reactive power. The validity of the proposed EWOA-PI controllers is verified by comparing the results with whale optimization algorithm (WOA) based PI controllers under severe grid disturbances. The LVRT capability is performed using the MATLAB program.

关键词： PI control Doubly fed induction generators Rotors whale optimization algorithms Steady-state Voltage control optimization

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An Improved whale optimization Algorithm for Ocean Wireless Sensor Network Coverage Simulation

An Improved Whale Optimization Algorithm for Ocean Wireless ...

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

作者： Zhongqiang Yang Thelma Domingo Palaoag Technology and Computer Science University of the Cordilleras Baguio the Philippines College of Electronic and Information Engineering University of Beibu Gulf Qinzhou City China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

Wireless sensor network (WSN) is widely used in fields such as marine environmental protection, and their nodes are usually deployed randomly, which can easily lead to low coverage in the monitoring area. On the basis of whale optimization Algorithm (WOA), a new improved algorithm (IWOA) is proposed to solve the optimization deployment of WSN nodes and improve the communication performance of marine wireless sensor networks. The IWOA algorithm improves the coverage performance of network signals by adjusting the probability values of contraction and spiral update behaviors, as well as dynamically adjusting the step size of whale position updates. Using MATLAB to simulate the signal coverage of wireless sensor networks in a limited area, with 40 sensor nodes, IWOA improves the coverage of WSN by 2.99% compared to WOA. The experimental results show that IWOA algorithm can effectively improve the coverage performance of nodes and enhance the coverage of WSN.

关键词： Wireless sensor networks Spirals Heuristic algorithms Oceans whales whale optimization algorithms Protection optimization Monitoring Matlab

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Vehicle charging station location based on improved whale optimization algorithm

Vehicle charging station location based on improved whale op...

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International Conference on Computer Engineering and Applications (ICCEA)

作者： Zhaodi Li Qingqing Liu Kaixuan Yu Department of Computer and Information Engineering Henan University Kaifeng China Department of Computer Science and Art Henan Light Industry Vocational College ZhengZhou China State Key Laboratory of Shield Machine and Boring Technology ZhengZhou China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

In order to better explore a method for solving the problem of electric vehicle charging station location planning, and improve the shortcomings of whale algorithm such as weak stability, sometimes slow convergence speed and easy to fall into local extremum, a whale optimization algorithm with quasi-opposition-based learning and enhanced search mechanism was proposed. Then, the algorithm flow is given, and the CEC2017 test function set is simulated by six representative algorithms in multiple dimensions. The results show that the convergence speed, optimization accuracy and solution stability of the improved algorithm are significantly improved, and it has good convergence performance. In order to solve the problem of electric vehicle public charging station location and service area division, an improved whale algorithm and Voronoi diagram are used to jointly solve the location planning of charging station, so as to achieve the purpose of global optimization. The application results of the last example show that the improved whale algorithm can effectively solve the charging station location problem and has superior optimization performance.

关键词： Accuracy Urban areas whales Charging stations Search problems whale optimization algorithms Stability analysis

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Differential whale optimization Algorithm

Differential Whale Optimization Algorithm

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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 Intelligent Solution to Fractional Order Controller Tuning Problem Using whale optimization Method

An Intelligent Solution to Fractional Order Controller Tunin...

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Silchar Subsection Conference (SILCON), IEEE

作者： Sayan Kundu Mahadeb Soo Soumadip Chatterjee Soham Dey Dept. of Electrical Engineering Dr. B.C. Roy Engineering College Durgapur India

ISBN: (数字)9798331540821

ISBN: (纸本)9798331540838

In this paper, design and implementation of various fractional order controllers designed using an artificial intelligence based modern meta-heuristic optimization algorithm has been demonstrated for solving a real world control problem of the magnetic levitation system (Maglev). fractional order calculus (FOC) plays a significant role in control theory, offering several advantages in modeling and controlling complex systems. FOC has gained a lot of research interest as a state-of-the-art control system design procedure. Basically fractional order controllers are a sub-application of FOC which provide accurate modelling, enhanced system performance, better stability and improved robustness compared to traditional integer order controllers. Therefore this study is concentrated on design and comparative analysis of fractional order (FO) lead, lag-lead and PID controllers based on a newly developed optimization algorithm known as whale optimization Algorithm (WOA). The objective function for controller parameter optimization is formulated using a standard error based performance metric namely integral square error (ISE). The simulation analysis signifies that the optimum performance is exhibited by the proposed FOPID controller.

关键词： Trajectory tracking System performance Stability criteria Metaheuristics Lead Control systems whale optimization algorithms Tuning Standards Magnetic levitation

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An Improved Beluga whale optimization Algorithm by Collaborative Strategies for Multi-Threshold Image Segmentation

An Improved Beluga Whale Optimization Algorithm by Collabora...

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International Conference on Computer Supported Cooperative Work in Design

作者： Mengran Liu Hui Xu Qinyue Wu Chenbing Dong School of Computer Science Hubei University of Technology Wuhan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Multi-threshold segmentation techniques usually rely on exhaustive search, leading to an exponential growth in computational complexity with the increase in the number of thresholds. The combination of swarm intelligence and image segmentation is a promising approach to improve segmentation efficiency and reduce computational cost. Thus, this paper then proposes an Improved Beluga whale optimization (IBWO) algorithm incorporating collaborative strategies for multi-threshold image segmentation. As for the proposed IBWO algorithm, the following collaborative strategies are utilized. The logistic chaos mapping strategy can increase the randomness of the initial beluga population, the dynamic nonlinear equilibrium factor strategy can better balance the exploratory and exploitation phases, and the dynamic pinhole imaging strategy can increase the diversity of the population and prevent the population from falling into a local optimum. The objective function is minimum cross entropy, and experiments use benchmark images at different threshold levels to evaluate the method's performance and compare the IBWO algorithm with several other classical algorithms. The experimental results show that the proposed IBWO algorithm outperforms the comparative methods in all three image quality metrics, which are PSNR, SSIM, and FSIM.

关键词： Measurement Image segmentation Heuristic algorithms Sociology Collaboration Linear programming whale optimization algorithms

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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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