Urmia lake water depth modeling using extreme learning machine-improved grey wolf optimizer hybrid algorithm

Sales, Ali Kozekalani; Gül, Enes; Safari, Mir Jafar Sadegh; Gharehbagh, Hadi Ghodrat; Vaheddoost, Babak

doi:10.1007/s00704-021-03771-1

Cited by 17 publications

(2 citation statements)

References 76 publications

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“…The convergence time of the GWO algorithm in solving task scheduling is related to the distance between the grey wolf leadership and the optimal solution. If the adaptability of the leadership is high enough, that is, if it is close to the optimal solution individual, it indicates that the convergence speed of the algorithm is faster [25][26]. The classic GWO algorithm generates initial positions for each dimension through random functions in the initial stage.…”

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confidence: 99%

Optimization Strategy of Classification Model Based on Weighted Implicit Optimal Extreme Learning Machine

Zhang,

Wang

2024

IEEE Access

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Classification algorithms are one of the important research topics in the artificial intelligence, widely applied in various scientific and engineering fields. Extreme learning machine is a single hidden layer feed-forward neural network algorithm. Compared with traditional neural network models, the training speed and the generalization ability are also better. In terms of methodology, this study first innovatively improves the traditional Grey Wolf Optimization (GWO) algorithm to enhance its convergence and search ability. Specific improvement measures include implementing the reverse learning strategy to reduce the initial dependence of the algorithm on population distribution, and adding exploration perception strategy to enhance the global search ability of the algorithm by calculating heuristic factors, so as to identify the global optimal solution more effectively. The results showed that the improved W-DH-ELM model had excellent performance on multiple standard data sets. In particular, the average accuracy was more than 90%, which was significantly higher than other benchmark classification models. In terms of operation efficiency, the running time of the new model on different data sets was significantly reduced, accounting for less than 25%, and the lowest running time was only 4.89%. These experimental indicators verify the effectiveness of the introduced intelligent optimization algorithm in improving the performance of traditional ELM model without changing the original model structure. The improved W-DH-ELM model not only maintains the fast training characteristics of ELM, but also has higher accuracy and stability, which shows its superiority in dealing with complex classification tasks. In summary, the weighted double hidden layer extreme learning machine optimized by the improved GWO proposed in this study has significant advantages in classification problems, providing a new perspective for future machine learning applications and research.

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confidence: 99%

Optimization Strategy of Classification Model Based on Weighted Implicit Optimal Extreme Learning Machine

Zhang,

Wang

2024

IEEE Access

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“…DLH search strategy improves the balance between local search and global search and keeps the diversity of population. In recent applied studies (Diab et al, 2022, Yesilbudark, 2021, Sales et al, 2021, IGWO's superiority in solving practical problems has been demonstrated. IGWO mainly consists of three stages: initialization (Step1), move (Step2), and select and update (Step3).…”

Section: Weight Optimization Algorithmmentioning

confidence: 99%

A Combined model based on data decomposition and multi-model weighted optimization for precipitable water vapor forecasting

Zhang

Kou

Ren

et al. 2022

Preprint

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Water shortage is a major problem facing the world. Artificial precipitation enhancement is an effective way to improve precipitation conversion rate, but the selection of artificial precipitation enhancement operation timing is the main difficulty, and the precipitable water vapor(PWV) is a major index. The variation of PWV is nonlinear and unstable due to complex factors, especially in the Qilian mountains in the northeastern part of the Qinghai-Tibet Plateau, so it is difficult to predict it accurately. Therefore, based on the analysis of the observed data of microwave radiometer in Qilian Mountains, a new combined model is constructed which considers both data decomposition and prediction of several single models in this research. In the data preprocessing stage, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique is used to decompose and de-noise the PWV sequence. In the prediction stage, four neural network with unique characteristics, back propagation neural network (BPNN), long short term memroy (LSTM), bidirectional gated recurrent unit (BiGRU) and temporal convolutional network (TCN), are selected to predict the decomposed data respectively. A variant of gray Wolf optimization algorithm (IGWO) is used to determine the optimal weight of the model, and finally the comprehensive predicted value is obtained by weighting calculation. Through the analysis of experimental results, in the longest 15-step prediction, compared with CEEMDAN-BP, CEEMDAN-LSTM, CEEMDAN-BiGRU, CEEMDAN-TCN, the prediction accuracy can be improved by 54.17%, 35.05%, 22.38%, 23.86%, respectively. Other step size prediction also achieves the highest prediction accuracy.

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Bearing Fault Diagnosis Using Extreme Learning Machine Based on Artificial Gorilla Troops Optimizer

Isham

Saufi

Hasan

et al. 2023

Lecture Notes in Electrical Engineering

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Urmia lake water depth modeling using extreme learning machine-improved grey wolf optimizer hybrid algorithm

Cited by 17 publications

References 76 publications

Optimization Strategy of Classification Model Based on Weighted Implicit Optimal Extreme Learning Machine

Optimization Strategy of Classification Model Based on Weighted Implicit Optimal Extreme Learning Machine

A Combined model based on data decomposition and multi-model weighted optimization for precipitable water vapor forecasting

Bearing Fault Diagnosis Using Extreme Learning Machine Based on Artificial Gorilla Troops Optimizer

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