Enhancing 4G/LTE Network Path Loss Prediction with PSO-GWO Hybrid Approach by Messaoud Garah, Nabil Boukhennoufa

“…The best model for estimating the measured path loss is then optimized using three well-known evolutionary algorithms: Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES). …”
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A data-driven state identification method for intelligent control of the joint station export system by Guangli Xu, Yifu Wang, Zhihao Zhou, Yifeng Lu, Liangxue Cai

“…In this paper, a combination of Particle Swarm Optimization (PSO) and Gray Wolf Optimizer (GWO) is proposed to optimize the Backpropagation Neural Network (BP) model (PSO-GWO-BP) and a pressure drop prediction model for the joint station export system is established using PSO-GWO-BP. …”
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Neuro-evolutionary models for imbalanced classification problems by Israa Al-Badarneh, Maria Habib, Ibrahim Aljarah, Hossam Faris

“…The utilized algorithms are the Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and the Salp Swarm Algorithm (SSA). …”
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Application of a Supervised Learning Machine for Accurate Prognostication of Hydrogen Contents of Bio-Oil by Binghui Xu, Tzu-Chia Chen, Danial Ahangari, S. M. Alizadeh, Marischa Elveny, Jeren Makhdoumi

“…The support vector machine algorithm optimized by the grey wolf optimization method has been used in modeling this end. …”
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Application of Improved LSTM Model in Runoff Simulation in Arid Region of Northwest China: A Case Study of the Zuli River by SONG Haiping, TANG Yiran, DANG Wentao, WANG Yibo

“…Using observed runoff, precipitation, and monthly mean temperature data from 1980 to 2020, the research incorporated feature engineering, combined with extreme-value post-processing and mixed loss function optimization. On this basis, the grey wolf optimization (GWO) algorithm was used to optimize the parameters of the LSTM-Attention model, and the GWO-LSTM-Attention model was constructed, enhancing the models' capability to capture the region's complex runoff mechanisms. …”
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Prediction of Chemical Gas Emissions Based on Ecological Environment by Guobin Chen, Shijin Li

“…This paper proposes a gray wolf optimization algorithm based on chaotic search strategy combined with extreme learning machine to predict chemical emission gases, taking a 330 MW pulverized coal-fired boiler as a test object and establishing chemical emissions of CNGWO-ELM. …”
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A grid-based sectoring for energy-efficient wireless sensor networks by Nubunga Ishaya, Mustapha Aminu Bagiwa

“…This research improves CH selection by organizing sensor nodes into square grid clusters and employing a routing algorithm for randomized CH selection. Game theory (GT) and Ad hoc on Demand Vectors (AODV) were used to choose the optimal routing path, while Grey Wolf Optimization (GWO) was used to determine the optimal CHs. …”
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Introducing a Novel Method to Identify the Future Trend of Nikkei 225 Stock Price in Order to Reduce Investment Risk by Freyr Björgvinsson

“…This study proposes a new incorporation of hyperparameter optimization algorithms into machine learning techniques, such as Genetic Algorithms, Battle Royale Optimization, and Grey Wolf Optimization, for stock price prediction. …”
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RESEARCH OF EARLY FAULT FEATURE EXTRACTION OF SOLAR WHEEL BASED ON PARAMETRIC ADAPTIVE ICEEMDAN AND MCKD by ZHAO Naizhuo, ZHAO Yumeng, MEN Chengfu

“…In order to solve the problem of difficult to accurately extract early faults of solar wheels under the strong noise background, an improved grey wolf algorithm (newGWO) was proposed to optimize and improve the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and the maximum correlated kurtosis deconvolution (MCKD) for early fault feature extraction of solar wheels.NewGWO was used to optimize the selection of parameters of the white noise amplitude weight and noise addition times that affected the decomposition effect.The fault vibration signal was decomposed by newGWO-ICEEMDAN, and the minimum envelope entropy was selected as the fitness function to obtain several related modal components.Then, the envelope spectrum peak factor was selected as the best modal component index.MCKD signals optimized by newGWO were enhanced for the selected optimal intrinsic mode function （IMF） components. …”
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Interpretable prediction model for hand-foot-and-mouth disease incidence based on improved LSTM and XGBoost by Xiao LI, Shuyu HE, Yan PENG, Rongxin YANG, Lu TAO, Tingqi LOU, Wenqi HE

“…In order to address the issues of low accuracy and poor interpretability in existing HFMD incidence prediction models, in this paper, we propose an interpretable prediction model, namely, ARIMA–LSTM–XGBoost, which integrates multiple meteorological factors with Autoregressive integrated moving average model (ARIMA), Long short-term memory (LSTM), Extreme gradient boosting (XGBoost), Grey wolf optimizer (GWO), Genetic algorithm (GA) and Shapley additive explanations (SHAP). …”
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Estimating Economic Insights: A Machine Learning Method for Estimating the Shanghai Stock Exchange by Reza Seifi Majdar, Seyed Hadi Seyed Hatami

“…EMD is one of the methods for the decomposition of nonstationary and nonlinear time series data into simpler components. The optimization techniques used are Slime mould algorithm (SMA) and Grey Wolf Optimization (GWO) because of their efficiency in fine-tuning model parameters. …”
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An intelligent fault diagnosis model for bearings with adaptive hyperparameter tuning in multi-condition and limited sample scenarios by Jianqiao Li, Zhihao Huang, Liang Jiang, Yonghong Zhang

“…To address these issues, this paper presents an advanced diagnosis method using a hybrid Grey Wolf Algorithm (HGWA)-optimized convolutional neural network (CNN) and Bidirectional long short-term memory (BiLSTM) architecture. …”
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Cost-benefit analysis in demand response with penalty and grid management using blockchain by Manikandan Ramasamy, Thenmalar Kaliannan, Saravanakumar Ramasamy

“…Two distinct optimization methods such as the gray wolf optimization algorithm and particle swarm optimization are employed to solve the optimization model. …”
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Fault Diagnosis of Rolling-Element Bearing Using Multiscale Pattern Gradient Spectrum Entropy Coupled with Laplacian Score by Xiaoan Yan, Ying Liu, Peng Ding, Minping Jia

“…To address this problem, a novel approach entitled multiscale pattern gradient spectrum entropy (MPGSE) is further implemented to extract fault features across multiple scales, where its key parameters are determined adaptively by grey wolf optimization (GWO). Meanwhile, a Laplacian score- (LS-) based feature selection strategy is employed to choose the sensitive features and establish a new feature set. …”
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Task Allocation and Path Planning Method for Unmanned Underwater Vehicles by Feng Liu, Wei Xu, Zhiwen Feng, Changdong Yu, Xiao Liang, Qun Su, Jian Gao

“…First, we introduce a task allocation mechanism based on an Improved Grey Wolf Algorithm (IGWA). This mechanism comprehensively considers factors such as target value, distance, and UUV capability constraints to achieve efficient and reasonable task allocation among UUVs. …”
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Using SVM Classifier and Micro-Doppler Signature for Automatic Recognition of Sonar Targets by Abbas Saffari, Seyed Hamid Zahiri, Navid Khozein Ghanad

“…For a more fair comparison, multilayer perceptron neural network with two back-propagation (MLP-BP) training methods and gray wolf optimization (MLP-GWO) algorithm were used. …”
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Study on vibration characteristic of battery pack of electric excavator under non-stationary random excitation by LI Zhaojun, LI Feibiao, WANG Bo, ZHAO Ming, WU Fangming

“…The research shows that reconstructing road excitation signals based on wavelet transform and Grey Wolf Optimization-Variational Mode Decomposition (GWO-VMD) signal analysis algorithm can effectively reflect the characteristics of road excitation. …”
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Design of Nonlinear PID and FOPID Controllers for Electronic Throttle Valve Plate’s Position by Mohamed Jasim Mohamed, Luay Thamir Rasheed

“…However, all these control schemes above have been studied with and without considering the technique of manipulating the windup problem or antiwindup. A metaheuristic optimization technique, namely, the grey wolf optimization (GWO) algorithm, is introduced for optimizing the controllers’ parameters while minimizing the integral of the cube time square error (IT^3SE) cost function. …”
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Machine learning for defect condition rating of wall wooden columns in ancient buildings by Yufeng Li, Wu Ouyang, Zhenbo Xin, Houjiang Zhang, Shuqi Sun, Dian Zhang, Wenbo Zhang

“…The RBF neural network model achieved the highest accuracy (94.57 %) on the feature fusion dataset, while Grey Wolf Optimizer (GWO) optimization further improved accuracy to 96.74 %. …”
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A Study on Hyperspectral Soil Moisture Content Prediction by Incorporating a Hybrid Neural Network into Stacking Ensemble Learning by Yuzhu Yang, Hongda Li, Miao Sun, Xingyu Liu, Liying Cao

“…First, raw hyperspectral data are processed by removing edge noise and standardization. Then, the gray wolf optimization (GWO) algorithm is adopted to optimize a convolutional neural network (CNN), and a gated recurrent unit (GRU) and an attention mechanism are added to construct a hybrid neural network model (GWO–CNN–GRU–Attention). …”
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