Vibration State Identification of Hydraulic Units Based on Improved Artificial Rabbits Optimization Algorithm

Cao, Qingjiao; Wang, Liying; Zhao, Weiguo; Yuan, Zhouxiang; Liu, Anran; Gao, Yanan; Ye, R.P.

doi:10.3390/biomimetics8020243

Cited by 8 publications

(2 citation statements)

References 30 publications

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“…For optimal selection of the features, the IARO algorithm is employed. The ARO technique [26] is presented based on the survival strategies followed by the rabbits. It involves three search strategies such as the detour foraging strategy, random hiding strategy, and energy shrink strategy.…”

Section: Feature Selection Using Iaro Algorithmmentioning

confidence: 99%

Improved Artificial Rabbits Optimization with Ensemble Learning-Based Traffic Flow Monitoring on Intelligent Transportation System

Ragab,

Abdushkour,

Maghrabi

et al. 2023

Sustainability

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Traffic flow monitoring plays a crucial role in Intelligent Transportation Systems (ITS) by dealing with real-time data on traffic situations and allowing effectual traffic management and optimization. A typical approach used for traffic flow monitoring frequently depends on collection and analysis of the data through a manual process that is not only resource-intensive, but also a time-consuming process. Recently, Artificial Intelligence (AI) approaches like ensemble learning demonstrate promising outcomes in numerous ITS applications. With this stimulus, the current study proposes an Improved Artificial Rabbits Optimization with Ensemble Learning-based Traffic Flow Monitoring System (IAROEL-TFMS) for ITS. The primary intention of the proposed IAROEL-TFMS technique is to employ the feature subset selection process with optimal ensemble learning so as to predict the traffic flow. In order to accomplish this, the IAROEL-TFMS technique initially designs the IARO-based feature selection approach to elect a set of features. In addition, the traffic flow is predicted using the ensemble model that comprises a Gated Recurrent Unit (GRU), Long Short-term Memory (LSTM), and Bidirectional Gated Recurrent Unit (BiGRU). Finally, the Grasshopper Optimization Algorithm (GOA) is applied for the adjustment of the optimum hyperparameters of all three DL models. In order to highlight the improved prediction results of the proposed IAROEL-TFMS algorithm, an extensive range of simulations was conducted. The simulation outcomes imply the supremacy of the IAROEL-TFMS methodology over other existing approaches with a minimum RMSE of 16.4539.

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Section: Feature Selection Using Iaro Algorithmmentioning

confidence: 99%

Improved Artificial Rabbits Optimization with Ensemble Learning-Based Traffic Flow Monitoring on Intelligent Transportation System

Ragab,

Abdushkour,

Maghrabi

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…Ma et al [13] proposed an identification method based on singular value decomposition (SVD) and MPSO-SVM. Cao et al [14] suggested an improved artificial bee colony optimization algorithm (IARO) with adaptive weight adjustment. IARO optimized support vector machine (SVM) to create an identification model to classify and identify vibration signals in different stages.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Probabilistic Neural Network Model with the Improved Manta Ray Foraging Optimization Algorithm to Identify Pressure Fluctuation Signal Features

Liu,

Wang,

Yan

et al. 2024

Biomimetics

Self Cite

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To improve the identification accuracy of pressure fluctuation signals in the draft tube of hydraulic turbines, this study proposes an improved manta ray foraging optimization (ITMRFO) algorithm to optimize the identification method of a probabilistic neural network (PNN). Specifically, first, discrete wavelet transform was used to extract features from vibration signals, and then, fuzzy c-means algorithm (FCM) clustering was used to automatically classify the collected information. In order to solve the local optimization problem of the manta ray foraging optimization (MRFO) algorithm, four optimization strategies were proposed. These included optimizing the initial population of the MRFO algorithm based on the elite opposition learning algorithm and using adaptive t distribution to replace its chain factor to optimize individual update strategies and other improvement strategies. The ITMRFO algorithm was compared with three algorithms on 23 test functions to verify its superiority. In order to improve the classification accuracy of the probabilistic neural network (PNN) affected by smoothing factors, an improved manta ray foraging optimization (ITMRFO) algorithm was used to optimize them. An ITMRFO-PNN model was established and compared with the PNN and MRFO-PNN models to evaluate their performance in identifying pressure fluctuation signals in turbine draft tubes. The evaluation indicators include confusion matrix, accuracy, precision, recall rate, F1-score, and accuracy and error rate. The experimental results confirm the correctness and effectiveness of the ITMRFO-PNN model, providing a solid theoretical foundation for identifying pressure fluctuation signals in hydraulic turbine draft tubes.

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Advances in Artificial Rabbits Optimization: A Comprehensive Review

Anka,

Agaoglu,

Nematzadeh

et al. 2024

Arch Computat Methods Eng

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Vibration State Identification of Hydraulic Units Based on Improved Artificial Rabbits Optimization Algorithm

Cited by 8 publications

References 30 publications

Improved Artificial Rabbits Optimization with Ensemble Learning-Based Traffic Flow Monitoring on Intelligent Transportation System

Improved Artificial Rabbits Optimization with Ensemble Learning-Based Traffic Flow Monitoring on Intelligent Transportation System

Optimizing the Probabilistic Neural Network Model with the Improved Manta Ray Foraging Optimization Algorithm to Identify Pressure Fluctuation Signal Features

Advances in Artificial Rabbits Optimization: A Comprehensive Review

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