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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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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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Electrical Impedance Tomography Based on grey wolf Optimized Radial Basis Function Neural Network

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MICROMACHINES 2022年第7期13卷 1120-1120页

作者： Wang, Guanghua Feng, Di Tang, Wenlai Nanjing Normal Univ Sch Elect & Automat Engn Jiangsu Key Lab 3D Printing Equipment & Mfg Nanjing 210023 Peoples R China Southeast Univ Sch Mech Engn Jiangsu Key Lab Design & Manufacture Micronano Bi Nanjing 211189 Peoples R China Nanjing Univ Dept Sports Med & Adult Reconstruct Surg State Key Lab Pharmaceut Biotechnol Nanjing Drum Tower HospAffiliated HospMed Sch Nanjing 210008 Peoples R China Nanjing Inst Intelligent High End Equipment Ind C Nanjing 210042 Peoples R China

Electrical impedance tomography (EIT) is a non-invasive, radiation-free imaging technique with a lot of promise in clinical monitoring. However, since EIT image reconstruction is a non-linear, pathological, and ill-posed issue, the quality of the reconstructed images needs constant improvement. To increase image reconstruction accuracy, a grey wolf optimized radial basis function neural network (GWO-RBFNN) is proposed in this paper. The grey wolf algorithm is used to optimize the weights in the radial base neural network, determine the mapping between the weights and the initial position of the grey wolf, and calculate the optimal position of the grey wolf to find the optimal solution for the weights, thus improving the image resolution of EIT imaging. COMSOL and MATLAB were used to numerically simulate the EIT system with 16 electrodes, producing 1700 simulation samples. The standard Landweber, RBFNN, and GWO-RBFNN approaches were used to train the sets separately. The obtained image correlation coefficient (ICC) of the test set after training with GWO-RBFNN is 0.9551. After adding 30, 40, and 50 dB of Gaussian white noise to the test set, the attained ICCs with GWO-RBFNN are 0.8966, 0.9197, and 0.9319, respectively. The findings reveal that the proposed GWO-RBFNN approach outperforms the existing methods when it comes to image reconstruction.

关键词： electrical impedance tomography grey wolf optimization algorithm image reconstruction radial basis function neural networks

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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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Dynamics analysis and parameter optimization of a vibration absorber with geometrically nonlinear inerters

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JOURNAL OF VIBRATION AND CONTROL 2024年第21-22期30卷 5031-5046页

作者： Shen, Yongjun Sui, Peng Shijiazhuang Tiedao Univ Dept Mech Engn Shijiazhuang Peoples R China Shijiazhuang Tiedao Univ State Key Lab Mech Behav & Syst Safety Traff Engn Shijiazhuang Peoples R China Shijiazhuang Tiedao Univ Dept Mech Engn 17 North Second Ring East Rd Shijiazhuang 050043 Hebei Peoples R China Shijiazhuang Tiedao Univ State Key Lab Mech Behav & Syst Safety Traff Engn 17 North Second Ring East Rd Shijiazhuang 050043 Hebei Peoples R China

The inerter-based dynamic vibration absorber (DVA) is a promising technique for vibration control. In most studies, the inerter is usually mounted in the same direction as that of the motion, which may not adequately reflect the vibration suppression capability and the two-terminal inertial feature of the inerter, and even weakens the performance of vibration absorbers. Considering the potentiality of improving the control performance by unconventional mounting methods, a rarely studied structure of geometrically nonlinear inerters is introduced into the vibration absorber system. This novel vibration absorber is presented to investigate the following issues, that is, the unknown coupled dynamical behavior between the complex nonlinear force with inertia, damping, and stiffness terms generated by this structure and the vibration absorber system, the possibility of vibration absorption facilitated by this structure, and the full utilization of the two-terminal inertial feature of the inerter. The approximate solutions of the system are obtained using the harmonic balance method. The influence of each variable on the system response is analyzed, and the system parameters are optimized by using the grey wolf algorithm. In addition to the ordinary resonance phenomenon, there are also dynamic features such as soft characteristic jump behavior and response loops at certain parameter range. As a nonlinear system, this model is more stable than the nonlinear energy sink (NES). The optimized amplitude-frequency curves are equal-peak stable, similar to the linear vibration absorbers. The robustness of system parameters is high for small inerter-mass ratios or excitation amplitudes, which is better than DVAs. Compared to the classical linear vibration absorber, NES, and improved NES, the vibration suppression capacity and damping bandwidth of this model are enhanced. In comparison, it is also found that this model has smaller optimum parameters than the classical NES and equiv

关键词： inerter geometrical nonlinearity nonlinear vibration absorber parameter optimization grey wolf optimization algorithm

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A Novel Optimal Planning and Operation of Smart Cities by Simultaneously Considering Electric Vehicles, Photovoltaics, Heat Pumps, and Batteries

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PROCESSES 2024年第9期12卷 1816-1816页

作者： Shokri, Masoud Niknam, Taher Sarvarizade-Kouhpaye, Miad Pourbehzadi, Motahareh Javidi, Giti Sheybani, Ehsan Dehghani, Moslem Shiraz Univ Technol Dept Elect & Elect Engn Shiraz *** Iran Fars Reg Elect Co Shiraz *** Iran Comillas Pontif Univ Inst Res Technol IIT Sch Engn ICAI Madrid 28015 Spain Univ S Florida Muma Coll Business Sch Informat Syst & Management Tampa FL 33620 USA

A smart city (SC) includes different systems that are highly interconnected. Transportation and energy systems are two of the most important ones that must be operated and planned in a coordinated framework. In this paper, with the complete implementation of the SC, the performance of each of the network elements has been fully analyzed;hence, a nonlinear model has been presented to solve the operation and planning of the SC model. In the literature, water treatment issues, as well as energy hubs, subway systems (SWSs), and transportation systems have been investigated independently and separately. A new method of subway and electric vehicle (EV) interaction has resulted from stored energy obtained from subway braking and EV parking. Hence, considering an SC that simultaneously includes renewable energy, transportation systems such as the subway and EVs, as well as the energy required for water purification and energy hubs, is a new and unsolved challenge. In order to solve the problem, in this paper, by presenting a new system of the SC, the necessary planning to minimize the cost of the system is presented. This model includes an SWS along with plug-in EVs (PEVs) and different distributed energy resources (DERs) such as Photovoltaics (PVs), Heat Pumps (HPs), and stationary batteries. An improved grey wolf optimizer has been utilized to solve the nonlinear optimization problem. Moreover, four scenarios have been evaluated to assess the impact of the interconnection between SWSs and PEVs and the presence of DER technologies in the system. Finally, results were obtained and analyzed to determine the benefits of the proposed model and the solution algorithm.

关键词： grey wolf optimization algorithm optimal planning and operation regenerative braking energy smart city

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