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optimization Strategy of Classification Model Based on Weighted Implicit Optimal Extreme Learning Machine

IEEE ACCESS 2024年 12卷 74169-74184页

作者： Zhang, Na Wang, Xiaofeng Henan Finance Univ Sch Comp & Artificial Intelligence Zhengzhou 450046 Peoples R China SEGi Univ Grad Sch Business GSB Kota Damansara 47810 Petaling Jaya Malaysia

Classification algorithms are one of the important research topics in the artificial intelligence, widely applied in various scientific and engineering fields. Extreme learning machine is a single hidden layer feed-forward neural network algorithm. Compared with traditional neural network models, the training speed and the generalization ability are also better. In terms of methodology, this study first innovatively improves the traditional grey wolf optimization (GWO) algorithm to enhance its convergence and search ability. Specific improvement measures include implementing the reverse learning strategy to reduce the initial dependence of the algorithm on population distribution, and adding exploration perception strategy to enhance its global search ability by calculating heuristic factors, so as to identify the global optimal solution more effectively. The results showed that the improved W-DH-ELM model had excellent performance on multiple standard data sets. In particular, the average accuracy was more than 90%, which was significantly higher than other benchmark classification models. In terms of operation efficiency, the running time of the new model on different data sets was significantly reduced, accounting for less than 25%, and the lowest running time was only 4.89%. These experimental results verify the effectiveness of the introduced intelligent optimization algorithm in improving the performance of traditional ELM model without changing the original model structure. The improved W-DH-ELM model not only maintains the fast training performance of ELM, but also has higher accuracy and stability, which shows its superiority in dealing with complex classification tasks. In summary, the weighted two-hidden layer extreme learning machine optimized by the improved GWO proposed in this study has significant advantages in classification problems, providing a new perspective for future machine learning applications and research.

关键词： optimization Classification algorithms Convergence Predictive models Heuristic algorithms Prediction algorithms Feature extraction Learning systems Reverse logistics Two-hidden layer extreme learning machine grey wolf optimization algorithm classification model reverse learning perception strategy

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Effective virtual inertia control using inverter optimization method in renewable energy generation

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ENERGY EXPLORATION & EXPLOITATION 2021年第6期39卷 2103-2125页

作者： Niu, Shuanbao Qu, Linan Lin, Hsiung-Cheng Fang, Wanliang Xi An Jiao Tong Univ Sch Elect Engn Xian Peoples R China Northwest Branch State Grid Corp China Xian Peoples R China China Elect Power Res Inst Nanjing Nanjing Peoples R China Hebei Univ Technol Sch Elect Engn Tianjin Peoples R China Natl Chin Yi Univ Technol Dept Elect Engn Taichung 41170 Taiwan Xi An Jiao Tong Univ Sch Elect Engn Xian Peoples R China

The high-level penetration of intermittent renewable power generation may limit power system inertia, resulting in system frequency instability in increasing power converter-based energy sources. To resolve this problem, virtual inertia control using distributed gray wolf optimization (DGWO) method in a synchronous generator is simulated under a distinct output fluctuation condition. First, the DGWO algorithm was established to achieve a local and global balance solution, and standard test functions were employed to verify the model convergence. Second, the key parameters that determine the effect of the virtual inertia controller in the power grid were analyzed. A DGWO-based optimization strategy to stabilize inertia was also developed. Finally, simulation results using step and random loads under a high permeability level are provided to verify the effectiveness of the proposed model. In the step load disturbance, the system can recover from the disturbance point to the stable point after 3 s under the regulation of the proposed control strategy, which is reduced by 18 s compared with the traditional control method. In the random load test, it takes only 12 s, 63 s less than the traditional one. Accordingly, the power system frequency can be stabilized more quickly from a disturbance state to a stable stage.

关键词： Virtual synchronous generator renewable energy generation grey wolf optimization algorithm frequency regulation parameter optimization

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Model identification and strategy application for Solid Oxide Fuel Cell using Rotor Hopfield Neural Network based on a novel optimization method

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2020年第51期45卷 27694-27704页

作者： Ba, Shusong Xia, Dong Gibbons, Edward M. Univ Sci & Technol China Sch Management Dept Stat & Finance Hefei 230026 Anhui Peoples R China Univ Nova Gorica Sch Engn & Management Vipavska Cesta 5000 Nova Gorica Slovenia

The novelty of this paper is to suggest an effective method according to the application of the Rotor Hopfield Neural Network optimized by the grey wolf optimization (GWO) method for the identification of the Solid Oxide Fuel Cell (SOFC) model. In this literature, the basic required metrics to present the transient models of Solid Oxide Fuel Cell are defined. The proposed model is a hybrid model that is composed of the Rotor Hopfield Neural Network (RHNN) optimized by the GWO algorithm. The hybrid RHNN-GWO model, including a steady-state RHNN Neural Network, ensured by an optimization method. The RHNN algorithm is presented to assess the metrics of the RHNN-GWO model. In contrast to the wavering, the Mean Squared Error (MSE) for the RHNN-GWO model is calculated by 0.0017. The presented model results are examined with some well-known model results. The lowest values for Mean Squared Error belongs to the RHNN-GWO model. Also, the proposed model conserves a tremendous value of calculation time compared to the other models. Also, the proposed model shows a good agreement with SOFC results with lower computational difficulty. For 5000 samples, the variation of the voltage is in the [320, 360] V interval, which completely follows the reference voltage of the SOFC. (C) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： SOFC Model identification Neural network Rotor Hopfield Neural Network grey wolf optimization algorithm

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FGMTS: Fractional grey wolf optimizer for multi-objective task scheduling strategy in cloud computing

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018年第1期35卷 831-844页

作者： Sreenu, Karnam Malempati, Sreelatha Acharya Nagarjuna Univ ANU Coll Engn Dept Comp Sci & Engn Guntur Andhra Prades India RVR & JC Coll Engn Dept Comp Sci & Engn Guntur Andhra Prades India

Task scheduling on heterogeneous data centers of cloud computing environment is a challenging problem. The efficiency of the cloud depends on the adopted task scheduling strategy. The task scheduling algorithm schedules the required task resources of application in the cloud platform. Even though many algorithms are presented for task scheduling;they consider only the minimum objectives for the trade-off of optimal scheduling. In this paper, a multi-objective task scheduling strategy is proposed for the task scheduling problem in the cloud network as an NP-hard optimization problem. In order to solve the scheduling problem, Fractional grey wolf Multi-objective optimization-based Task Scheduling strategy (FGMTS) is newly proposed for scheduling tasks in the cloud. The proposed FGMTS algorithm is the combination of the existing fractional theory and grey wolf Optimizer algorithm. Also, the multi-objective function is newly formulated to solve the multi-objective scheduling problem. The fitness function for the proposed optimization considers the parameters, such as Execution time, Communication time, Execution cost, Communication cost, Energy, and Resource utilization for optimal scheduling. The experimentation of the proposed task scheduling strategy is carried out over two cloud setups. The performance of proposed system is validated over the existing techniques, such as PSO, GA, and GWO using the metrics considered in the multi-objective formulation function. The experimental results show that the proposed FGMTS-Task scheduling scheme allocates the resource for all incoming task requests while preserving the performance of the cloud with an increase in the profit.

关键词： Cloud computing task scheduling fractional theory grey wolf optimization algorithm multi-objective constraints

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Point Cloud Registration algorithm Based on the grey wolf Optimizer

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IEEE ACCESS 2020年 8卷 143375-143382页

作者： Feng, Yaqing Tang, Jialin Su, Binghua Su, Qinglang Zhou, Zhuang Beijing Inst Technol Opt Engn Zhuhai 519088 Peoples R China Beijing Inst Technol Minist Educ China Key Lab Photoelect Imaging Technol & Syst Zhuhai 519088 Peoples R China Beijing Inst Technol Fac Informat Technol Zhuhai 519088 Peoples R China Beijing Inst Technol Beijing 100081 Peoples R China City Univ Macau Res Inst Data Sci Macau Peoples R China

In view of the long computation time and low registration accuracy of the current point cloud registration algorithm, a point cloud registration algorithm based on the grey wolf optimizer (GWO) is proposed, denoted PCR-GW. The algorithm uses the centralization method to solve the translation matrix and then simplifies the points of the initial point cloud models by using the intrinsic shape signatures (ISS) feature. Next, various parameters of the rotation matrix are obtained via the GWO algorithm by employing the quadratic sum of the distances between corresponding points in the simplified point cloud as the objective function. Finally, the point cloud registration process is completed by using the obtained transformation matrix. By conducting a registration experiment on the point cloud library model and comparing PCR-GW with the traditional algorithms, the algorithm proposed in this article is shown to be promising for improving the computation speed and registration accuracy.

关键词： Point cloud registration feature point extraction grey wolf optimization algorithm

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Localization of multiple leaks in a fluid pipeline based on ultrasound velocity and improved GWO

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2020年 137卷 1-7页

作者： Lang Xianming Li Ping Zhang Baocun Cao Jiangtao Guo Ying Kan Zhe Lu Siyu Liaoning Shihua Univ Sch Informat & Control Engn Fushun 113001 Peoples R China Shandong Nat Gas Pipeline Co Ltd Jinan 250013 Shandong Peoples R China Shenyang Longchang Pipeline Survey Ctr Shenyang 110000 Peoples R China

It is difficult to effectively locate the position of multiple leaks in fluid pipelines;therefore, a localization method for multiple leak positions is proposed based on ultrasound velocity and improved grey wolf optimization algorithm (GWO). First, the mathematical relationship between ultrasound velocity and the pressure signal inside a pipeline is established. Second, the ultrasound velocity is decomposed by local mean decomposition (LMD), and the inflection point of the ultrasound velocity by using a wavelet transform after denoising is extracted. Then, the simulated annealing GWO (SAGWO) method is proposed to improve the performance of GWO which is easy to converge to local optimum. Finally, the inflection time of the ultrasound velocity is obtained at the ends of a pipeline with multiple leaks, and an objective function is established to estimate the localization of the multiple leaks by SAGWO. The field experiment demonstrates that the proposed method can effectively and accurately locate multiple leak positions in a fluid pipeline. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Ultrasound velocity Local mean decomposition Wavelet transform Multiple leaks localization Simulated annealing grey wolf optimization algorithm

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A cold chain logistics distribution optimization model: Beijing-Tianjin-Hebei region low-carbon site selection

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INDUSTRIAL MANAGEMENT & DATA SYSTEMS 2024年第11期124卷 3138-3163页

作者： Zhang, Liyi Fu, Mingyue Fei, Teng Lim, Ming K. Tseng, Ming-Lang Tianjin Univ Commerce Tianjin Peoples R China Tianjin Univ Finance & Econ Tianjin Peoples R China Univ Glasgow Adam Smith Business Sch Glasgow Scotland Asia Univ Inst Innovat & Circular Econ Taichung Taiwan China Med Univ Dept Med Res Taichung Taiwan Univ Kebangsaan Malaysia UKM Grad Sch Business Bangi Malaysia Khon Kaen Univ Dept Ind Engn Khon Kaen Thailand

PurposeThis study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center ***/methodology/approachThis study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center *** optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain ***/valueThis study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.

关键词： Low-carbon cold chain logistics Distribution center site selection model grey wolf optimization algorithm Artificial fish swarm algorithm

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A Real-Time Maximum Power Points Tracking strategy Consider Power-to-Average Ratio Limiting for Wave Energy Converter

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IEEE ACCESS 2022年 10卷 48039-48048页

作者： Zhu, Lixun Yao, Zhaoyang Li, Wei Shanghai Maritime Univ Dept Logist Engn Shanghai 201306 Peoples R China Tongji Univ Dept Elect Engn Shanghai 200092 Peoples R China

Based on the mechanical model of the point absorption Wave Energy Converter (WEC), a real-time complex conjugate (CC) maximum power point tracking (MPPT) control strategy consider power peak-to-average ratio (PAR) limiting under irregular waves is proposed. In the proposed MPPT strategy, the system can not only track the maximum power, but also limit the PAR of the output power to improve the controlled generator's utilization rate. Two parameters, R-epot and X-pto, that determine the command current of the PMSG and the PAR, are introduced in the mathematical model of the system to control the system. And they are identified by the grey wolf optimization (GWO) algorithm. During each half cycle of the sea wave, the parameters R-pto and X-pto will be real time updated to satisfy the control strategy. In addition, the system parameters A, B-rad and F(omega) related to wave frequency are also calculated by the advanced quantitative wave analysis (AQWA) software. Finally, the proposed strategy is verified by the comparison on the simulation and the experimental results.

关键词： Wave energy converter maximum power point tracking grey wolf optimization algorithm permanent magnet synchronous generator

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Deep machine learning with grey wolf algorithm and central deference Kalman filter based broken rotor bars detection in induction motor

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IET RENEWABLE POWER GENERATION 2022年第16期16卷 3519-3530页

作者： Zaniani, Ali Amiri Nafar, Mehdi Simab, Mohsen Islamic Azad Univ Marvdasht Branch Dept Elect Engn Marvdasht Iran

The present study tries to propose a new method which is using Central deference Kalman Filter (CDKF) as input index of deep machine learning (DML), for simulating state estimation and broken rotor bars (BRBs) diagnosis in induction motors (IMs). In addition, an advanced selective ensemble (DML) method applying the grey wolf optimization (GWO) algorithm to diagnose the BRBs in IMs is proposed in the following study. Here, in the initial step, in order to take sufficient datum within the usual efficiency, it is needed to train the DML network. The outcomes indicate that the offered scheme can more accurately and powerfully detect diverse forms of BRBs with an accuracy of more than 98%. And also, Filter precision is enhanced via changing the sigma points of the filter, however, the stability of the filter enhances more because of its utilization, and the CDKF is further stable and precise in comparison to the Unscented Kalman filter (UKF). The CDKF performance is assessed to estimate the speed and is used as input index of DML to diagnose the broken rotor bar in IMs. The obtained outcomes prove the performance of this combined scheme.

关键词： Central deference Kalman Filter CDKF Filter precision learning (artificial intelligence) broken rotor bars diagnosis deep machine learning induction motors BRBs broken rotor bar broken rotor bars detection Unscented Kalman filter grey wolf optimization algorithm rotors central deference Kalman filter induction motor Kalman filters state estimation advanced selective ensemble method IMs grey wolf algorithm fault diagnosis input index DML network nonlinear filters

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A hybrid clustering algorithm based on improved GWO and KHM clustering

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第4期42卷 3227-3240页

作者： Xue, Feng Liu, Yongbo Ma, Xiaochen Pathak, Bharat Liang, Peng Southwest Jiao Tong Univ Sch Transportat & Logist Chengdu Sichuan Peoples R China Southwest Jiao Tong Univ Grad Sch Tangshan Tangshan Hebei Peoples R China Natl Engn Lab Integrated Transportat Big Data App Chengdu Sichuan Peoples R China Eindhoven Univ Technol Transportat & Urban Planning Grp Eindhoven Netherlands

To solve the problem that the K-means algorithm is sensitive to the initial clustering centers and easily falls into local optima, we propose a new hybrid clustering algorithm called the IGWOKHM algorithm. In this paper, we first propose an improved strategy based on a nonlinear convergence factor, an inertial step size, and a dynamic weight to improve the search ability of the traditional grey wolf optimization (GWO) algorithm. Then, the improved GWO (IGWO) algorithm and the K-harmonic means (KHM) algorithm are fused to solve the clustering problem. This fusion clustering algorithm is called IGWOKHM, and it combines the global search ability of IGWO with the local fast optimization ability of KHM to both solve the problem of the K-means algorithm's sensitivity to the initial clustering centers and address the shortcomings of KHM. The experimental results on 8 test functions and 4 University of California Irvine (UCI) datasets show that the IGWO algorithm greatly improves the efficiency of the model while ensuring the stability of the algorithm. The fusion clustering algorithm can effectively overcome the inadequacies of the K-means algorithm and has a good global optimization ability.

关键词： grey wolf optimization algorithm nonlinear convergence factor inertial step size dynamic weight K-harmonic means clustering hybrid clustering

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