TY - JOUR
T1 - Enhancing multilayer perceptron neural network using archive-based harris hawks optimizer to predict gold prices
AU - Abu-Doush, Iyad
AU - Ahmed, Basem
AU - Awadallah, Mohammed A.
AU - Al-Betar, Mohammed Azmi
AU - Rababaah, Aaron Rasheed
N1 - Publisher Copyright:
© 2023 The Author(s)
PY - 2023/4/17
Y1 - 2023/4/17
N2 - The success of the Multi-Layer Perceptron Neural Network (MLP) relies on carefully configuring its weights and biases to promising values. The gradient descent technique is usually used to select the optimal MLP configuration. Such a technique can stuck in local optima and its convergence towards promising regions in the search space is slow. Harris Hawks optimizer (HHO) is a recently proposed metaheuristic technique that mimics the harris’ hawks behavior to chase and catch prey. In this paper, the algorithm search process is enhanced by introducing an external archive that saves the best solutions to be used in the next iterations. Gold price prediction is essential for future investment planning and decision-making in mining projects. The gold price is predicted using a multilayer perceptron (MLP) neural network (NN) that has the optimal control parameters obtained using the improved version of Harris Hawks optimizer (HHO) with an external archive which is called (AHHO-NN). The input features with a complicated relationship can influence gold price prediction models such as inflation rate, precious metals prices, and other features. In this sense, the literature suggests a specific reduced number of features for the MLP-NN model to yield high-quality prediction results. In addition to the prediction model, two feature reduction methods are used to select the proper list of the input features for the MLP-NN model to forecast gold price namely, Pearson's correlation and a newly proposed categorized correlation. Surprisingly, new features not mentioned in the literature are discovered and some are discarded. The time series dataset used has been extracted from several sources and pre-processed to fit the proposed model. Furthermore, the prediction results have been evaluated using several measures such as prediction accuracy, Mean square error (MSE), Root mean square error (RMSE), Mean absolute error (MAE), and average absolute relative deviation (AARD). The performance of the proposed AHHO-NN model is compared against four swarm intelligence algorithms (i.e., HHO-NN, JAYA-NN, MFO-NN, PSO-NN) as well as four classical machine learning techniques (i.e., linear regression (LR), MLP Regressor (MLP), RANSAC Regressor (RANSAC), and TheilSen Regressor (TR)). Interestingly, the proposed AHHO-NN is able to comparatively produce high-quality prediction results for gold prices with relatively high accuracy. This proves the viability of the proposed AHHO-NN model as well as the proper selection of the primitive input features.
AB - The success of the Multi-Layer Perceptron Neural Network (MLP) relies on carefully configuring its weights and biases to promising values. The gradient descent technique is usually used to select the optimal MLP configuration. Such a technique can stuck in local optima and its convergence towards promising regions in the search space is slow. Harris Hawks optimizer (HHO) is a recently proposed metaheuristic technique that mimics the harris’ hawks behavior to chase and catch prey. In this paper, the algorithm search process is enhanced by introducing an external archive that saves the best solutions to be used in the next iterations. Gold price prediction is essential for future investment planning and decision-making in mining projects. The gold price is predicted using a multilayer perceptron (MLP) neural network (NN) that has the optimal control parameters obtained using the improved version of Harris Hawks optimizer (HHO) with an external archive which is called (AHHO-NN). The input features with a complicated relationship can influence gold price prediction models such as inflation rate, precious metals prices, and other features. In this sense, the literature suggests a specific reduced number of features for the MLP-NN model to yield high-quality prediction results. In addition to the prediction model, two feature reduction methods are used to select the proper list of the input features for the MLP-NN model to forecast gold price namely, Pearson's correlation and a newly proposed categorized correlation. Surprisingly, new features not mentioned in the literature are discovered and some are discarded. The time series dataset used has been extracted from several sources and pre-processed to fit the proposed model. Furthermore, the prediction results have been evaluated using several measures such as prediction accuracy, Mean square error (MSE), Root mean square error (RMSE), Mean absolute error (MAE), and average absolute relative deviation (AARD). The performance of the proposed AHHO-NN model is compared against four swarm intelligence algorithms (i.e., HHO-NN, JAYA-NN, MFO-NN, PSO-NN) as well as four classical machine learning techniques (i.e., linear regression (LR), MLP Regressor (MLP), RANSAC Regressor (RANSAC), and TheilSen Regressor (TR)). Interestingly, the proposed AHHO-NN is able to comparatively produce high-quality prediction results for gold prices with relatively high accuracy. This proves the viability of the proposed AHHO-NN model as well as the proper selection of the primitive input features.
KW - Gold price prediction
KW - Harris Hawks optimization algorithm
KW - Multilayer perceptron neural network
KW - Predicting
KW - Swarm intelligence optimizers
KW - Training neural network
UR - http://www.scopus.com/inward/record.url?scp=85153369224&partnerID=8YFLogxK
U2 - 10.1016/j.jksuci.2023.101557
DO - 10.1016/j.jksuci.2023.101557
M3 - Article
AN - SCOPUS:85153369224
SN - 1319-1578
VL - 35
JO - Journal of King Saud University - Computer and Information Sciences
JF - Journal of King Saud University - Computer and Information Sciences
IS - 5
ER -