WEIGHTED GREY WOLF OPTIMIZER WITH IMPROVED CONVERGENCE RATE IN TRAINING MULTI-LAYER PERCEPTRON TO SOLVE CLASSIFICATION PROBLEMS

The accurate prediction of the solar diffuse fraction (DF), sometimes called the diffuse ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse irradiance research is discussed and then three robust, machine learning (ML) models are examined using a large dataset (almost eight years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid adaptive network-based fuzzy inference system (ANFIS), a single multi-layer perceptron (MLP) and a hybrid multi-layer perceptron grey wolf optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various solar and DF irradiance data, from Spain. The results were then evaluated using frequently used evaluation criteria, the mean absolute error (MAE), mean error (ME) and the root mean square error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance in both the training and the testing procedures.

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Predicting soil electrical conductivity using multi-layer perceptron integrated with grey wolf optimizer

Journal of Geochemical Exploration ◽

10.1016/j.gexplo.2020.106639 ◽

2021 ◽

Vol 220 ◽

pp. 106639

Author(s):

Amirhosein Mosavi ◽

Saeed Samadianfard ◽

Sabereh Darbandi ◽

Narjes Nabipour ◽

Sultan Noman Qasem ◽

...

Keyword(s):

Electrical Conductivity ◽

Grey Wolf Optimizer ◽

Multi Layer Perceptron ◽

Soil Electrical Conductivity ◽

Grey Wolf

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Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction

10.20944/preprints202009.0377.v1 ◽

2020 ◽

Author(s):

Randall Claywell ◽

Nadai Laszlo ◽

Felde Imre ◽

Amir Mosavi

Keyword(s):

Fuzzy Inference System ◽

Fuzzy Inference ◽

Evaluation Criteria ◽

Absolute Error ◽

Grey Wolf Optimizer ◽

Multi Layer Perceptron ◽

Grey Wolf ◽

Inference System ◽

Diffuse Fraction ◽

Testing Procedures

The accurate prediction of the solar Diffuse Fraction (DF), sometimes called the Diffuse Ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse Irradiance research is discussed and then three robust, Machine Learning (ML) models, are examined using a large dataset (almost 8 years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid Adaptive Network-based Fuzzy Inference System (ANFIS), a single Multi-Layer Perceptron (MLP) and a hybrid Multi-Layer Perceptron-Grey Wolf Optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various Solar and Diffuse Fraction (DF) irradiance data, from Spain. The results were then evaluated using two frequently used evaluation criteria, the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance, in both the training and the testing procedures.

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Flood Classification Based on a Fuzzy Clustering Iteration Model with Combined Weight and an Immune Grey Wolf Optimizer Algorithm

Water ◽

10.3390/w11010080 ◽

2019 ◽

Vol 11 (1) ◽

pp. 80 ◽

Cited By ~ 3

Author(s):

Qiang Zou ◽

Li Liao ◽

Yi Ding ◽

Hui Qin

Keyword(s):

Fuzzy Clustering ◽

Evaluation Criteria ◽

Sensitivity Coefficient ◽

Risk Identification ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Classification Problems ◽

Real Time Scheduling ◽

Iterative Model ◽

Time Scheduling

Flood classification is an important basis for flood forecasting, flood risk identification, flood real-time scheduling, and flood resource utilization. However, flood classification results may be not reasonable due to uncertainty, the fuzziness of evaluation indices, and the demerit of not comprehensively considering the index weight. In this paper, based on the fuzzy clustering iterative model, a sensitivity coefficient was applied to combine the subjective and objective weights into a combined weight, then the fuzzy clustering iterative model with combined weight (FCI-CW) was proposed for flood classification. Moreover, an immune grey wolf optimizer algorithm (IGWO) based on the standard grey wolf optimizer algorithm and an immune clone selection operator was proposed for the global search of the optimal fuzzy clustering center and the sensitivity coefficient of FCI-CW. Finally, simulation results at Nanjing station and Yichang station demonstrate that the proposed methodology, i.e., FCI-CW combined with IGWO, is reasonable and reliable, can effectively deal with flood classification problems with better fitness and a comprehensive consideration of the subjective and objective aspects, and has great application potential in sorting, evaluation, and decision-making problems without evaluation criteria.

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Using Orthogonal Grey Wolf Optimizer with Mutation for Training Multi-Layer Perceptron Neural Network

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2016.5319 ◽

2016 ◽

Vol 13 (7) ◽

pp. 4544-4556 ◽

Cited By ~ 1

Author(s):

Sen Zhang ◽

Yongquan Zhou ◽

Junmin Song ◽

Chengyan Zhao

Keyword(s):

Neural Network ◽

Grey Wolf Optimizer ◽

Multi Layer Perceptron ◽

Grey Wolf

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Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction

10.21203/rs.3.rs-77142/v1 ◽

2020 ◽

Author(s):

Randall Claywell ◽

Laszlo Nadai ◽

Imre Felde ◽

Amir Mosavi

Keyword(s):

Fuzzy Inference System ◽

Fuzzy Inference ◽

Evaluation Criteria ◽

Absolute Error ◽

Grey Wolf Optimizer ◽

Multi Layer Perceptron ◽

Grey Wolf ◽

Inference System ◽

Diffuse Fraction ◽

Testing Procedures

Abstract The accurate prediction of the solar Diffuse Fraction (DF), sometimes called the Diffuse Ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse Irradiance research is discussed and then three robust, Machine Learning (ML) models, are examined using a large dataset (almost 8 years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid Adaptive Network-based Fuzzy Inference System (ANFIS), a single Multi-Layer Perceptron (MLP) and a hybrid Multi-Layer Perceptron-Grey Wolf Optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various Solar and Diffuse Fraction (DF) irradiance data, from Spain. The results were then evaluated using two frequently used evaluation criteria, the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance, in both the training and the testing procedures.

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Hybrid Grey Wolf Optimizer Using Elite Opposition-Based Learning Strategy and Simplex Method

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026817500122 ◽

2017 ◽

Vol 16 (02) ◽

pp. 1750012 ◽

Cited By ~ 30

Author(s):

Sen Zhang ◽

Qifang Luo ◽

Yongquan Zhou

Keyword(s):

Convergence Rate ◽

Simplex Method ◽

Learning Strategy ◽

Search Algorithm ◽

Gravitational Search Algorithm ◽

Population Diversity ◽

Flower Pollination Algorithm ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Opposition Based Learning

To overcome the poor population diversity and slow convergence rate of grey wolf optimizer (GWO), this paper introduces the elite opposition-based learning strategy and simplex method into GWO, and proposes a hybrid grey optimizer using elite opposition (EOGWO). The diversity of grey wolf population is increased and exploration ability is improved. The experiment results of 13 standard benchmark functions indicate that the proposed algorithm has strong global and local search ability, quick convergence rate and high accuracy. EOGWO is also effective and feasible in both low-dimensional and high-dimensional case. Compared to particle swarm optimization with chaotic search (CLSPSO), gravitational search algorithm (GSA), flower pollination algorithm (FPA), cuckoo search (CS) and bat algorithm (BA), the proposed algorithm shows a better optimization performance and robustness.

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Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction

10.31219/osf.io/6qybp ◽

2020 ◽

Author(s):

Randall Claywell ◽

Laszlo Nadai ◽

Felde Imre ◽

Amir Mosavi

Keyword(s):

Fuzzy Inference System ◽

Fuzzy Inference ◽

Evaluation Criteria ◽

Absolute Error ◽

Grey Wolf Optimizer ◽

Multi Layer Perceptron ◽

Grey Wolf ◽

Inference System ◽

Diffuse Fraction ◽

Testing Procedures

The accurate prediction of the solar Diffuse Fraction (DF), sometimes called the Diffuse Ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse Irradiance research is discussed and then three robust, Machine Learning (ML) models, are examined using a large dataset (almost 8 years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid Adaptive Network-based Fuzzy Inference System (ANFIS), a single Multi-Layer Perceptron (MLP) and a hybrid Multi-Layer Perceptron-Grey Wolf Optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various Solar and Diffuse Fraction (DF) irradiance data, from Spain. The results were then evaluated using two frequently used evaluation criteria, the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance, in both the training and the testing procedures.

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