Application of a grey wolf optimization-enhanced convolutional neural network and bidirectional gated recurrent unit model for credit scoring prediction. by Yetong Fang

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Prediction of compressive strength of fiber-reinforced concrete containing silica (SiO2) based on metaheuristic optimization algorithms and machine learning techniques by Hamed Shokrnia, Ashkan KhodabandehLou, Peyman Hamidi, Fedra Ashrafzadeh

“…So, this study integrates the ANFIS (adaptive neuro-fuzzy inference system) and ELM (extreme learning machine) machine learning models with three optimization algorithms, i.e., WCA (water cycle algorithm), PSO (particle swarm optimization), and GWO (grey wolf optimizer) to precisely estimate the CS of fiber-reinforced concrete (FRC) containing SiO2. …”
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APPLICATION OF IMPROVED GWO-SVM IN WIND TURBINE GEARBOX FAULT DIAGNOSIS by HU Xuan, LI Chun, YE KeHua, ZHANG WanFu

“…Then feature vectors are used to reduce dimensionality using isometric mapping. The improved gray wolf algorithm is used to optimize the support vector machine to diagnose the gearbox fault feature set after dimensionality reduction. …”
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Double layered expansion planning for virtual power plants considering virtual energy storage systems by Jianghai Ma, Xuanwen Gu, Yao Zhang, Jinming Gu, Wenjie Luo, Feng Gao

“…To improve computational efficiency, a hybrid Grey Wolf Optimization algorithm is employed for model solution. …”
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Fault Diagnosis of Oil Pumping Machine Retarder Based on Sound Texture-Vibration Entropy Characteristics and Gray Wolf Optimization-Support Vector Machine by Shutao Zhao, Ke Chang, Erxu Wang, Bo Li, Kedeng Wang, Qingquan Wu

“…Finally, joint eigenvectors were constructed and fed into SVM for learning. The gray wolf optimization (GWO) algorithm was used to optimize the parameters of the SVM model based on mixed kernel function, which reduces the impact of sensor frequency response, environmental noise, and load fluctuation disturbance on the accuracy of retarder fault diagnosis. …”
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Harmonized Integration of GWO and J-SLnO for Optimized Asset Management and Predictive Maintenance in Industry 4.0 by A. N. Arularasan, P. Ganeshkumar, Mohammad Alkhatib, Tahani Albalawi

“…The study encompasses the application of two different advanced optimization algorithms on asset management and predictive maintenance in Industry 4.0—Grey Wolf Optimization and Jaya-based Sea Lion Optimization (J-SLnO). …”
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Robust Control Frameworks for Heave Disturbance Suppression in Offshore Managed Pressure Drilling Environments by Atif Rehman, Rimsha Ghias, Iftikhar Ahmad, Hammad Iqbal Sherazi

“…Controller gains are optimized using an improved Grey Wolf Optimization algorithm, targeting the minimization of the Integral Time Absolute Error. …”
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Optimal tuning of PID controller for V/f control of linear induction motor using artificial biological intelligence by Vineet Shekher, Aayush Sisodiya, Ashutosh Kumar Sinha, Himanshu Harsh, Nirmala Soren

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Digital Industrial Design Method in Architectural Design by Machine Learning Optimization: Towards Sustainable Construction Practices of Geopolymer Concrete by Xiaoyan Wang, Yantao Zhong, Fei Zhu, Jiandong Huang

“…This study explores the predictive capabilities of soft computing models in estimating the compressive strength of GeC, utilizing multi-layer perceptron (MLP) neural networks and hybrid systems incorporating the Gannet Optimization Algorithm (GOA) and Grey Wolf Optimizer (GWO). …”
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Prediction of Provincial Energy Consumption Intensity and Estimation of Carbon Emission Reduction Potential Based on PSO-GWO by Fugui DONG, Meijuan XIA, Wanying LI

“…An energy intensity learning curve was constructed, which contains such three factors as economy, technology input and scale effect, and the grey wolf algorithm was used to improve the particle swarm optimization algorithm to optimize the fitting curves. …”
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Optimizing blood glucose predictions in type 1 diabetes patients using a stacking ensemble approach by Vincent B. Liu, Laura Y. Sue, Oscar Madrid Padilla, Yingnian Wu

“…Methods: The DiaTrend dataset includes 27,561 days of continuous glucose monitoring and 8220 days of insulin pump data for 54 patients with type 1 diabetes. Grey Wolf optimization was used to tune and evaluate three machine learning algorithms – Random Forest, LSTM, GRU – for blood glucose predictions, whose predictions were then combined into an XGBoost stacking ensemble meta-learner. …”
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Optimizing Unmanned Vehicle Navigation: A Hybrid PSO-GWO Algorithm for Efficient Route Planning by Gökhan Altun, İlhan Aydın

“…In the hybrid algorithm, the simplicity and powerful global search capabilities of the Particle Swarm Optimization (PSO) algorithm are combined with the strong exploration and local minimum avoidance features of the Grey Wolf Optimization (GWO) algorithm. …”
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Grey Wolf Resampling-Based Rao-Blackwellized Particle Filter for Mobile Robot Simultaneous Localization and Mapping by Yong Dai, Ming Zhao

“…An artificial intelligent grey wolf optimizer (GWO)-assisted resampling scheme is applied to the Rao-Blackwellized particle filter (RBPF) in the simultaneous localization and mapping (SLAM). …”
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Dynamic performance improvement method of energy storage DC/DC converter for grid-connected wind turbine with energy storage by Haonan SHI, Yichao SUN, Xiaobo TANG, Wei WANG, Dongmei YANG, Zheng WEI

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Kinematics Analysis of a 2PRP_aU-2PSS Parallel Mechanism by Zheng Xin, Du Li, Che Linxian, Li Jie, Huang Xin, Lu Jiahong, Yi Hongyu

“…Meanwhile, the nonlinear equations of forward displacement are transformed into one-dimensional equation, thus using the improved grey wolf optimization algorithm to solve this problem. …”
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Machine Learning-Driven Prediction of CO₂ Solubility in Brine: A Hybrid Grey Wolf Optimizer (GWO)-Assisted Gaussian Process Regression (GPR) Approach by Seyed Hossein Hashemi, Farshid Torabi, Paitoon Tontiwachwuthikul

“…In this study, Gaussian Process Regression (GPR) with eight different kernels was optimized using the Grey Wolf Optimizer (GWO) algorithm to model this important phase behavior. …”
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Reservoir Neural Network Computing for Time Series Forecasting in Aerospace: Potential Applications to Predictive Maintenance by Juan Manuel Rodríguez Riesgo, Juan Luis Cabrera Fernández

“…Coupling a reservoir neural network and a Grey Wolf optimization algorithm the system hyperparameters space is explored to find the configuration best suited to forecast the input sensor from the NASA CMAPSS dataset. …”
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Daily Runoff Prediction Model Based on Multivariate Variational Mode Decomposition and Correlation Reconstruction by DING Jie, TU Peng-fei, FENG Yu, ZENG Huai-en

“…[Conclusions](1) The MVMD decomposition method can control the number of decomposition layers, ensuring complete signal feature extraction without overfitting while improving processing speed.(2) Pearson correlation coefficient method enhances prediction accuracy through decomposed data classification.(3) The MEA-BP can improve signal-to-noise ratio, adapt to complex environments, enhance learning efficiency and generalization ability, and reduce computational complexity.(4) The GWO-ELM algorithm integrates grey wolf optimizer with extreme learning machine, providing a fast and adaptive solution for time-series prediction with reduced overfitting and improved efficiency.(5) The overall combined model can efficiently and stably process large amount of data while ensuring high accuracy.…”
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Research on low carbon welding scheduling based on production process by Rong Hua Meng, Zan Yang Wang, Wen Hui Zeng, Feng Guan, Ding Kun Lei, Zheng Jia Wu, Shao Hua Deng

“…Firstly, a bi-objective low-carbon welding scheduling mathematical model is established with minimizing makespan and carbon emission. Then, an improved Grey Wolf Optimizer (IGWO) with three strategies is designed to solve this multi-objective problem. …”
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Forecasting the potential of global marine shipping carbon emission under artificial intelligence based on a novel multivariate discrete grey model by Zirui Zeng, Junwen Xu, Shiwei Zhou, Yufeng Zhao, Yansong Shi

“…Furthermore, the optimal nonlinear parameters are determined by Grey Wolf optimization algorithm to improve the prediction performance, enhancing the model’s predictive performance. …”
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