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9th International Conference on Power and Renewable Energy

作者： Yang, Xingliang Wang, Yujie Sun, Zhendong Chen, Zonghai Univ Sci & Technol China Dept Automat Hefei Peoples R China

ISBN: (纸本)9798350377477;9798350377460

Accurately predicting the degradation of fuel cells and then extending their lifespan through reasonable management measures is crucial. This paper proposes a novel method that combines random forest regression with grey wolf optimization algorithm to predict the degradation of fuel cells. First, the measurement data is reconstructed using a Gaussian average weighted smoothing method to reduce the measurement noise. Then, a degradation model of fuel cells is established using random forest regression. Finally, the hyperparameters of the random forest are optimized using the grey wolf optimization algorithm to improve the accuracy of degradation prediction. The effectiveness of this method is verified through degradation experiments conducted at two different load currents. The test results demonstrate that proposed method can significantly improve the accuracy of degradation prediction and outperform other methods in terms of precision.

关键词： Remaining useful life random forest grey wolf optimizer algorithm machine learning method

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Remaining Useful Life Prediction for Fuel Cell Based on Support Vector Regression and grey wolf optimizer algorithm

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2022年第2期37卷 778-787页

作者： Chen, Kui Laghrouche, Salah Djerdir, Abdesslem Univ Bourgogne Franche Comte Belfort UTBM FEMTO ST UMR CNRS 6174 F-90400 Belfort France Univ Bourgogne Franche Comte Belfort UTBM FCLAB FR CNRS 3539 F-90400 Belfort France

Remaining useful life prediction is an important way to improve the durability and reduce the cost of the proton exchange membrane fuel cell. This paper presents a novel method to predict the remaining useful life of proton exchange membrane fuel cell under different load currents based on support vector regression and grey wolf optimizer algorithm. The proposed method considers the influence of 17 operating conditions and historical voltage. Firstly, the measured data are reconstructed through robust locally weighted smoothing method to reduce the calculation amount and filter disturbances. Then, support vector regression with fewer hyperparameters is used to establish the degradation model. Finally, the hyperparameters of support vector regression are optimized through grey wolf optimizer algorithm to improve the accuracy of degradation prediction. The proposed method is validated by two degradation experiments under different load currents. The test results show that grey wolf optimizer algorithm can effectively improve the accuracy of degradation prediction based on support vector regression. Compared with other methods, the proposed method has the highest accuracy. The proposed method can predict the fuel cell degradation with a mean absolute percentage error of less than 0.3%. The proposed method can predict the remaining useful life of 492 hours.

关键词： Degradation Fuel cells Prediction algorithms Voltage measurement Support vector machines Predictive models Hydrogen Proton exchange membrane fuel cell remaining useful life support vector regression grey wolf optimizer algorithm machine learning method robust locally weighted smoothing method

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Improved Least Squares Support Vector Machine Model Based on grey wolf optimizer algorithm for Predicting CO2-Crude Oil Minimum Miscibility Pressure

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ENERGY TECHNOLOGY 2024年第5期12卷 2301139-2301139页

作者： Li, Jia-Qi Bian, Xiao-Qiang Southwest Petr Univ Petr Engn Sch Chengdu 610500 Peoples R China

The minimum miscibility pressure (MMP) is an important reference parameter in the study of CO2 oil drive systems. In response to the problems of time-consuming and costly prediction of MMP by conventional experimental methods, an improved least squares support vector machine (LSSVM) model based on grey wolf optimizer (GWO) algorithm is proposed to predict the CO2-crude oil MMP. Based on Pearson correlation analysis, reservoir temperature, C5+ molecular weight, intermediate component mole fraction, and volatile component mole fraction are selected as independent variables of the model, and MMP is the dependent variable. A total of 51 MMP experimental data are collected, of which 35 are used to fine-tune the model's parameters and 16 are used to verify the model's reliability. The high leverage point method is used to detect anomalies in all experimental data to check the reliability of the model, and the abnormality of only one piece of experimental data is identified. Finally, a comparison of the model with other intelligent models is found. The absolute relative deviation of the GWO-LSSVM model is 2.24% for the training data and 3.48% for the test data, which provides high adaptability and accuracy.

关键词： grey wolf optimizer algorithm least squares support vector machine minimum miscibility pressure

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Optimal design of groundwater pollution monitoring network based on a back-propagation neural network surrogate model and grey wolf optimizer algorithm under uncertainty

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2024年第2期196卷 132-132页

作者： Guo, Xinze Luo, Jiannan Lu, Wenxi Dong, Guangqi Pan, Zidong Jilin Univ Key Lab Groundwater Resources & Environm Minist Educ Changchun 130021 Peoples R China Jilin Univ Jilin Prov Key Lab Water Resources & Environm Changchun 130021 Peoples R China Jilin Univ Coll New Energy & Environm Changchun 130021 Peoples R China

In the optimal design of groundwater pollution monitoring network (GPMN), the uncertainty of the simulation model always affects the reliability of the monitoring network design when applying simulation-optimization methods. To address this issue, in the present study, we focused on the uncertainty of the pollution source intensity and hydraulic conductivity. In particular, we utilized simulation-optimization and Monte Carlo methods to determine the optimal layout scheme for monitoring wells under these uncertainty conditions. However, there is often a substantial computational load incurred due to multiple calls to the simulation model. Hence, we employed a back-propagation neural network (BPNN) to develop a surrogate model, which could substantially reduce the computational load. We considered the dynamic pollution plume migration process in the optimal design of the GPMN. Consequently, we formulated a long-term GPMN optimization model under uncertainty conditions with the aim of maximizing the pollution monitoring accuracy for each yearly period. The spatial moment method was used to measure the approximation degree between the pollution plume interpolated for the monitoring network and the actual plume, which could effectively evaluate the superior monitoring accuracy. Traditional methods are easily trapped in local optima when solving the optimization model. To overcome this limitation, we used the grey wolf optimizer (GWO) algorithm. The GWO algorithm has been found to be effective in avoiding local optima and in exploring the search space more effectively, especially when dealing with complex optimization problems. A hypothetical example was designed for evaluating the effectiveness of our method. The results indicated that the BPNN surrogate model could effectively fit the input-output relationship from the simulation model, as well as significantly reduce the computational load. The GWO algorithm effectively solved the optimization model and improved the so

关键词： Back-propagation neural network grey wolf optimizer algorithm Groundwater pollution Monitoring network design Simulation-optimization

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Estimates of greenhouse gas emission in Turkey with grey wolf optimizer algorithm-optimized artificial neural networks

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NEURAL COMPUTING & APPLICATIONS 2021年第20期33卷 13567-13585页

作者： Uzlu, Ergun Karadeniz Tech Univ Dept Civil Engn Fac Engn TR-61080 Trabzon Turkey

The main purpose of this study was to predict Turkey's future greenhouse gas (GHG) emissions using an artificial neural network (ANN) model trained by a grey wolf optimizer (GWO) algorithm. Gross domestic product, energy consumption, population, urbanization rate, and renewable energy production data were used as predictor variables. To probe the accuracy of the proposed model, the new ANN-GWO model's performance was compared with the performance of ANN-BP (back propagation), ANN-ABC (artificial bee colony), and ANN-TLBO (teaching-learning-based optimization) models using multiple error criteria. According to calculated error values, the ANN-GWO models predicted GHG emissions more accurately than classical ANN-BP, ANN-ABC, and ANN-TLBO models. According to the average relative error values calculated for the test set, ANN-GWO performs 32.23% better than ANN-BP, 35.29% better than ANN-ABC, and 19.33% better than ANN-TLBO. Using the ANN-GWO model, GHG emissions were forecasted out to 2030 under three different scenarios. The predictions obtained, consistent with a prior forecasting study in the literature, indicated that GHG emissions are expected to outpace official predictions (model prediction range for 2030, 956.97-1170.54 Mt CO2 equivalent). The present study demonstrated that GHG emissions can be predicted accurately with an ANN-GWO model, and that the GWO optimization method is advantageous for predicting future GHG emissions.

关键词： grey wolf optimizer algorithm Teaching– learning-based optimization algorithm Greenhouse gas emission Neural networks

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Solving multi-objective model of assembly line balancing considering preventive maintenance scenarios using heuristic and grey wolf optimizer algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 100卷 104183-104183页

作者： Meng, Kai Tang, Qiuhua Zhang, Zikai Yu, Chunlong Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China

The current assembly line balancing studies ignore the preventive maintenance (PM) of machines in some workstations, implying that the already-known PM information has been completely missed. Moreover, PM may bring about a production stoppage for a considerable time. Hence, this paper considers PM scenarios into the assembly line balancing problem to improve the production efficiency and smoothness simultaneously. For this multi-objective problem, a heuristic rule relying on the tacit knowledge is dug up via gene expression programming to obtain an acceptable solution quickly. Then, an enhanced grey wolf optimizer algorithm with two improvements is proposed to achieve Pareto front solutions. Specifically, a variable step-size decoding mechanism accelerates the speed of the algorithm;the specially-designed neighbor operators prevent the algorithm from trapping in local optima. Experiment results demonstrate that the discovered heuristic rule outperforms other existing rules;the joint of improvements endows the proposed meta-heuristic with significant superiority over three variants and other six well-known algorithms. Besides, a real-world case study is conducted to validate the discovered rule and the proposed meta-heuristic.

关键词： Assembly line balancing Preventive maintenance Heuristic rule grey wolf optimizer algorithm Gene expression programming

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Optimization of an auto drum fashioned brake using the elite opposition-based learning and chaotic k-best gravitational search strategy based grey wolf optimizer algorithm

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APPLIED SOFT COMPUTING 2022年 123卷

作者： Yuan, Yongliang Mu, Xiaokai Shao, Xiangyu Ren, Jianji Zhao, Yong Wang, Zhenxi Henan Polytech Univ Sch Mech & Power Engn Jiaozuo 454003 Peoples R China Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China Henan Polytech Univ Sch Safety Sci & Engn Jiaozuo 454003 Peoples R China Henan Polytech Univ Sch Comp Sci & Technol Jiaozuo 454003 Peoples R China

Highly non-linear optimization problems are widely found in many real-world engineering applications. To tackle these problems, a novel assisted optimization strategy, named elite opposition-based learning and chaotic k-best gravitational search strategy (EOCS), is proposed for the grey wolf optimizer (GWO) algorithm. In the EOCS based grey wolf optimizer (EOCSGWO) algorithm, the elite opposition-based learning strategy (EOBLS) is proposed to take full advantage of better-performing particles for optimization in the next generations. A chaotic k-best gravitational search strategy (CKGSS) is proposed to obtain the adaptive step to improve the global exploratory ability. The performance of the EOCSGWO is verified and compared with those of other seven meta-heuristic optimization algorithms using ten popular benchmark functions. Results show that the EOCSGWO is more competitive in accuracy and robustness, and obtains the first in ranking among the six optimization algorithms. Further, the EOCSGWO is employed to optimize the design of an auto drum fashioned brake. The results show that the braking efficiency factor can be improved by 28.412% compared with the initial design. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Parameter identification grey wolf optimizer algorithm Opposition-based learning Chaotic k-best gravitational search

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A Novel Hybrid Discrete grey wolf optimizer algorithm for Multi-UAV Path Planning

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2021年第3期103卷 49-49页

作者： Huang, Gewen Cai, Yanguang Liu, Jianqi Qi, Yuanhang Liu, Xiaozhou Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China Jiaying Univ Informat & Network Ctr Meizhou 514015 Guangdong Peoples R China Univ Elect Sci & Technol China Sch Comp Sci Zhongshan Inst Zhongshan 528402 Guangdong Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Sichuan Peoples R China Guangdong Univ Technol Sch Mat & Energy Guangzhou 510006 Guangdong Peoples R China

With the development of the fifth-generation wireless network, autonomous moving platforms such as unmanned aerial vehicles (UAV) have been widely used in modern smart cities. In some applications, the UAVs need to perform certain monitoring tasks within a specified time. However, due to the energy constraints of UAVs, such tasks require using multiple UAVs to monitor multiple points. To solve this practical problem, this paper proposes a multi-UAV path planning model with the energy constraint (MUPPEC). The MUPPEC considers the energy consumption of a UAV in different states, such as acceleration, cruising speed, deceleration, and hovering, and the main objective of the MUPPEC is to minimize the total monitoring time. Also, a hybrid discrete intelligence algorithm based on the grey wolf optimizer (HDGWO) is proposed to solve the MUPPEC. In the HDGWO, the discrete grey wolf update operators are implemented, and the integer coding and greedy algorithms are used to transform between the grey wolf space and discrete problem space. Furthermore, the central position operation and stagnation compensation grey wolf update operation are introduced to improve the global convergence ability, and a two-opt with azimuth is designed to enhance the local search ability of the algorithm. Experimental results show that the HDGWO can solve the MUPPEC effectively, and compared to the traditional grey wolf optimizer(GWO), the discrete operators and the two-opt local search strategy with azimuth can effectively improve the optimization ability of the GWO.

关键词： Unmanned aerial vehicles Path planning Hybrid intelligent algorithm grey wolf optimizer algorithm

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Hybrids of Support Vector Regression with grey wolf optimizer and Firefly algorithm for Spatial Prediction of Landslide Susceptibility

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REMOTE SENSING 2021年第24期13卷 4966-4966页

作者： Liu, Ru Peng, Jianbing Leng, Yanqiu Lee, Saro Panahi, Mahdi Chen, Wei Zhao, Xia Changan Univ Coll Geol Engn & Geomat Xian 710054 Peoples R China Korea Inst Geosci & Mineral Resources KIGAM Geosci Platform Res Div 124 Gwahak Ro Daejeon 34132 South Korea Korea Univ Sci & Technol Dept Geophys Explorat 217 Gajeong Ro Daejeon 34113 South Korea Kangwon Natl Univ Div Sci Educ Coll Educ 4-301 Gangwondaehak Gil Chuncheon Si 24341 South Korea Xian Univ Sci & Technol Coll Geol & Environm Xian 710054 Peoples R China

Landslides are one of the most frequent and important natural disasters in the world. The purpose of this study is to evaluate the landslide susceptibility in Zhenping County using a hybrid of support vector regression (SVR) with grey wolf optimizer (GWO) and firefly algorithm (FA) by frequency ratio (FR) preprocessed. Therefore, a landslide inventory composed of 140 landslides and 16 landslide conditioning factors is compiled as a landslide database. Among these landslides, 70% (98) landslides were randomly selected as the training dataset of the model, and the other landslides (42) were used to verify the model. The 16 landslide conditioning factors include elevation, slope, aspect, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, sediment transport index (STI), stream power index (SPI), topographic wetness index (TWI), normalized difference vegetation index (NDVI), landslide, rainfall, soil and lithology. The conditioning factors selection and spatial correlation analysis were carried out by using the correlation attribute evaluation (CAE) method and the frequency ratio (FR) algorithm. The area under the receiver operating characteristic curve (AUROC) and kappa data of the training dataset and validation dataset are used to evaluate the prediction ability and the relationship between the advantages and disadvantages of landslide susceptibility maps. The results show that the SVR-GWO model (AUROC = 0.854) has the best performance in landslide spatial prediction, followed by the SVR-FA (AUROC = 0.838) and SVR models (AUROC = 0.818). The hybrid models of SVR-GWO and SVR-FA improve the performance of the single SVR model, and all three models have good prospects for regional-scale landslide spatial modeling.

关键词： landslide susceptibility support vector regression algorithm grey wolf optimizer algorithm firefly algorithm hybrid model

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Forecasting the Carbon Price Using Extreme-Point Symmetric Mode Decomposition and Extreme Learning Machine Optimized by the grey wolf optimizer algorithm

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ENERGIES 2019年第5期12卷 950-950页

作者： Zhou, Jianguo Huo, Xuejing Xu, Xiaolei Li, Yushuo North China Elect Power Univ Dept Econ & Management 689 Huadian Rd Baoding 071003 Peoples R China

Due to the nonlinear and non-stationary characteristics of the carbon price, it is difficult to predict the carbon price accurately. This paper proposes a new novel hybrid model for carbon price prediction. The proposed model consists of an extreme-point symmetric mode decomposition, an extreme learning machine, and a grey wolf optimizer algorithm. Firstly, the extreme-point symmetric mode decomposition is employed to decompose the carbon price into several intrinsic mode functions and one residue. Then, the partial autocorrelation function is utilized to determine the input variables of the intrinsic mode functions, and the residue of the extreme learning machine. In the end, the grey wolf optimizer algorithm is applied to optimize the extreme learning machine, to forecast the carbon price. To illustrate the superiority of the proposed model, the Hubei, Beijing, Shanghai, and Guangdong carbon price series are selected for the predictions. The empirical results confirm that the proposed model is superior to the other benchmark methods. Consequently, the proposed model can be employed as an effective method for carbon price series analysis and forecasting.

关键词： extreme-point symmetric mode decomposition extreme learning machine grey wolf optimizer algorithm carbon price forecasting

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