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Novel grey wolf optimization based on modified differential evolution for numerical function optimization

APPLIED INTELLIGENCE 2020年第2期50卷 468-486页

作者： Luo, Jun Liu, Zewei Chongqing Univ Chongqing 400030 Peoples R China

grey wolf optimization algorithm (GWO) is a new swarm intelligence optimization algorithm proposed in recent years. Because of few control parameters and easy implementation, GWO is widely used in many fields. Compared with other common swarm optimization algorithms, it is more suitable for the global optimization problems. Nevertheless, the algorithm still has the shortcoming of low accuracy and slow convergence speed. In this paper, a novel hybrid optimization algorithm named MDE-GWO is proposed. Firstly, a JADE with opposition-based learning strategy algorithm (MDE) is embedded in GWO to enhance the ability of avoiding a local optimum. Notably, by introducing the opposition-based learning strategy, the search ability of the improved algorithm is increased greatly. Additionally, in order to balance the global and local search capabilities and speed up the convergence of the GWO algorithm, a leader moving-rate strategy is put forward. 28 typical benchmark functions are utilized to test the performance of the improved algorithm. The experimental results show that MDE-GWO has stronger advantages in search accuracy, stability and convergence speed in most cases.

关键词： grey wolf optimization algorithm Global optimization Opposition-based learning Leader moving-rate strategy Benchmark functions

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Emergency Supplies Demand Forecasting Based on Improved grey wolf algorithm 24

Emergency Supplies Demand Forecasting Based on Improved Grey...

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International Conference on algorithms, Software Engineering, and Network Security (ASENS)

作者： Zhang, Bo Li, Qiaochu Ding, Mengmeng Shenyang Univ Shenyang 110003 Liaoning Peoples R China

ISBN: (纸本)9798400709784

This paper proposes an improved grey wolf optimization (GWO) algorithm by introducing three enhancement strategies: parameter decrement improvement, position update improvement, and fitness evaluation reference strategy. These strategies aim to address the shortcomings of the basic GWO algorithm, including low solution accuracy, slow convergence speed, and susceptibility to local optima. Through comparative experiments, the effectiveness of the enhancement strategies is validated. The experimental results indicate that the Improved grey wolf optimization algorithm (DGWO), which integrates the three strategies, significantly enhances the performance of the basic GWO algorithm. This further demonstrates the efficient optimization capability of the improved algorithm, particularly in the context of accurate forecasting of emergency supplies demand.

关键词： grey wolf optimization algorithm Convergence factor Global optimization

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Short-Term Load Forecasting Technology with Distributed Energy Timing Uncertainty 7

Short-Term Load Forecasting Technology with Distributed Ener...

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7th International Conference on Energy, Electrical and Power Engineering (CEEPE)

作者： Ma, Jiangong Li, Qing Li, Ming Turgun, Yaxar Di, Qiang State Grid Xinjiang Elect Power Res Inst New Power Syst Technol Res Ctr Urumqi Peoples R China Xinjiang Univ Sch Elect Engn Urumqi Peoples R China

ISBN: (纸本)9798350375794;9798350375800

With the rapid growth of distributed photovoltaic scale, the impact of random fluctuation characteristics of its output on urban load is also increasing. Facing the load forecasting scenario of large-scale distributed PV access, this paper proposes a short-term load forecasting method considering the influence of distributed PV. The side load of the grid under photovoltaic access is the difference between the actual electricity load and photovoltaic output. Therefore, before constructing input data, this paper first adopts big data mining technology to analyze the characteristics of photovoltaic output and user side load and the correlation between them and their respective influencing factors. The influential factors with high correlation are selected as the input feature set of the load forecasting model through feature construction. Then, the LSTM neural network prediction model with self-attention mechanism is constructed to deeply explore the load sequence characteristics. grey wolf algorithm was used to optimize the prediction model, and the model with the best prediction effect was determined. The numerical results show that the proposed method can effectively improve the prediction accuracy of net load with distributed PV.

关键词： distributed photovoltaic correlation analysis Self-attention mechanism LSTM grey wolf optimization algorithm load forecasting

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A hybrid cluster-borderline SMOTE method for imbalanced data of rock groutability classification

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BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT 2022年第1期81卷 1-15页

作者： Li, Kai Ren, Bingyu Guan, Tao Wang, Jiajun Yu, Jia Wang, Kexiang Huang, Jicun Tianjin Univ State Key Lab Hydraul Engn Simulat & Safety 92 Weijin Rd Tianjin 300072 Peoples R China China Three Gorges Construct Engn Corp Yichang Peoples R China

Groutability classification is highly important for guaranteeing the safety and quality of grouting projects. However, the precision of groutability classification is inevitably influenced by imbalanced data, in which most fractured rock masses are groutable. Current studies cannot realize high-precision classification for minority classes without considering imbalanced data. Although synthetic minority oversampling technique (SMOTE) is the most influential oversampling method, it produces redundant samples and noise labels. To address these issues, a hybrid cluster-borderline SMOTE method (HCBS) is proposed in this paper. The weights of samples near the minority class center and border were improved to present the category feature, thereby solving the redundant samples problem of SMOTE. To restrain noise, the total samples were divided into different clusters by k-means, and a cluster with an imbalance ratio of more than one was selected to generate new samples. The negative majority samples near the minority class border were removed by variant borderline SMOTE to clean the dataset from noisy instances, and the original and produced minority samples were reduced to a certain ratio according to the majority samples. Finally, the classification precision of the proposed HCBS method was verified using random forest (RF) in grouting engineering, and the number of trees and split variables of the tree nodes were optimized using the grey wolf optimization (GWO) algorithm. The proposed HCBS-GRF method outperformed RF, GWO-optimized RF (GRF), SMOTE-GRF, density SMOTE-GRF, borderline density SMOTE-GRF, and other competitive methods, thereby providing the highest groutability classification accuracy with the least number of generated instances.

关键词： Groutability classification Imbalanced data SMOTE Oversampling k-means clustering Random forest grey wolf optimization algorithm

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Optimized Controller Gains Using grey wolf algorithm for Grid Tied Solar Power Generation with Improved Dynamics and Power Quality

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Chinese Journal of Electrical Engineering 2022年第2期8卷 75-85页

作者： Veramalla Rajagopal Danthurthi Sharath Gundeboina Vishwas Jampana Bangarraju Sabha Raj Arya Challa Venkatesh Department of Electrical and Electronics Engineering Kakatiya Institute of Technology and Science WarangalTelangana 506015India Department of Electrical and Electronics Engineering B V Raju Institute of TechnologyTelangana 502313India Department of Electrical Engineering Sardar Vallabhbhai National Institute of Technology SuratGujarat 395007India

This study proposes a control algorithm based on synchronous reference frame theory with unit templates instead of a phase locked loop for grid-connected photovoltaic(PV)solar system,comprising solar PV panels,DC-DC converter,controller for maximum power point tracking,resistance capacitance ripple filter,insulated-gate bipolar transistor based controller,interfacing inductor,linear and nonlinear *** dynamic performance of the grid connected solar system depends on the effect operation of the control algorithm,comprising two proportional-integral *** controllers estimate the reference solar-grid currents,which in turn generate pulses for the three-leg voltage source *** grey wolf optimization algorithm is used to optimize the controller gains of the proportional-integral controllers,resulting in excellent performance compared to that of existing optimization *** compensation for neutral current is provided by a star-delta transformer(non-isolated),and the proposed solar PV grid system provides zero voltage regulation and eliminates harmonics,in addition to load *** power extraction from the solar panel is achieved using the incremental conductance algorithm for the DC-DC converter supplying solar power to the DC bus capacitor,which in turn supplies this power to the grid with improved dynamics and *** solar system along with the control algorithm and controller is modeled using Simulink in Matlab 2019.

关键词： Control algorithm solar power generation DC-DC converter star-delta transformer maximum power point tracking power quality grey wolf optimization algorithm

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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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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 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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Two-stage deep learning hybrid framework based on multi-factor multi-scale and intelligent optimization for air pollutant prediction and early warning

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2022年第10期36卷 3417-3437页

作者： Wang, Jujie Xu, Wenjie Dong, Jian Zhang, Yue Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Collaborat Innovat Ctr Forecast & Evaluat Meteoro Nanjing 210044 Peoples R China

Effective prediction of air pollution concentrations is of great importance to both the physical and mental health of citizens and urban pollution control. As one of the main components of air pollutants, accurate prediction of PM2.5 can provide a reference for air pollution control and pollution warning. This study proposes an air pollutant prediction and early warning framework, which innovatively combines feature extraction techniques, feature selection methods and intelligent optimization algorithms. First, the PM2.5 sequence is decomposed into several subsequences using the complete ensemble empirical mode decomposition with adaptive noise, and then the new components of the subsequences with different complexity are reconstructed using fuzzy entropy. Then, the Max-Relevance and Min-Redundancy method is used to select the influencing factors of the different reconstructed components. Then, a two-stage deep learning hybrid framework is constructed to model the prediction and nonlinear integration of the reconstructed components using a long short-term memory artificial neural network optimized by the gray wolf optimization algorithm. Finally, based on the proposed hybrid prediction framework, effective prediction and early warning of air pollutants are achieved. In an empirical study in three cities in China, the prediction accuracy, warning accuracy and prediction stability of the proposed hybrid framework outperformed the other comparative models. The analysis results indicate that the developed hybrid framework can be used as an effective tool for air pollutant prediction and early warning.

关键词： Feature extraction Feature selection grey wolf optimization algorithm Two-stage intelligent optimization Air pollutant prediction and early warning

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