A Quick Gbest Guided Artificial Bee Colony Algorithm for Stock Market Prices Prediction

Shah, Habib; Tairan, Nasser; Garg, Harish; Ghazali, Rozaida

doi:10.3390/sym10070292

Cited by 28 publications

(24 citation statements)

References 34 publications

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“…Many combined allocations exist among three transfer functions with "tansig", "logsig", and "purelin". The BP model with double hidden layers can overcome the overfitting problem and achieve the optimal prediction effect when the algorithm "trainlm" was selected as the training function, the function "tansig" was selected as the transfer function from the input layer to the first hidden layer, the function "logsig" was selected as the transfer function from the first hidden layer to the second hidden layer, and the function "purelin" was selected as the activation function from the second hidden layer to the output layer [51,52]. The momentum factor, learning rate, maximum number of trainings, and expected error were set as 0.3, 0.01, 240, and 0.01, respectively.…”

Section: Initialization Of Model Parametersmentioning

confidence: 99%

Groundwater Level Prediction for the Arid Oasis of Northwest China Based on the Artificial Bee Colony Algorithm and a Back-propagation Neural Network with Double Hidden Layers

Zheng

et al. 2019

Water

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Groundwater is crucial for economic and agricultural development, particularly in arid areas where surface water resources are extremely scarce. The prediction of groundwater levels is essential for understanding groundwater dynamics and providing scientific guidance for the rational utilization of groundwater resources. A back propagation (BP) neural network based on the artificial bee colony (ABC) optimization algorithm was established in this study to accurately predict groundwater levels in the overexploited arid areas of Northwest China. Recharge, exploitation, rainfall, and evaporation were used as input factors, whereas groundwater level was used as the output factor. Results showed that the fitting accuracy, convergence rate, and stabilization of the ABC-BP model are better than those of the particle swarm optimization (PSO-BP), genetic algorithm (GA-BP), and BP models, thereby proving that the ABC-BP model can be a new method for predicting groundwater levels. The ABC-BP model with double hidden layers and a topology structure of 4-7-3-1, which overcame the overfitting problem, was developed to predict groundwater levels in Yaoba Oasis from 2019 to 2030. The prediction results of different mining regimes showed that the groundwater level in the study area will gradually decrease as exploitation quantity increases and then undergo a decline stage given the existing mining condition of 40 million m3/year. According to the simulation results under different scenarios, the most appropriate amount of groundwater exploitation should be maintained at 31 million m3/year to promote the sustainable development of groundwater resources in Yaoba Oasis.

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Section: Initialization Of Model Parametersmentioning

confidence: 99%

Groundwater Level Prediction for the Arid Oasis of Northwest China Based on the Artificial Bee Colony Algorithm and a Back-propagation Neural Network with Double Hidden Layers

Zheng

et al. 2019

Water

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“…The first half of the colony consists of the employed bees and the second includes the unemployed. From the different numerical and statistical performance measure demonstrate that the ABC algorithm is competitive with other types of meta-heuristic and typical algorithms [55]- [57]. The technical duties of the employed and unemployed artificial bees are given in details.…”

Section: A Bio-inspired Artificial Bee Colonymentioning

confidence: 99%

“…It has been successfully used for solving different statistical, mathematical, science and engineering problems. The exploration and exploitation are the famous process of ABC by employed and unemployed bees [58]- [62].…”

Section: B Best-so-far Artificial Bee Colony Algorithmmentioning

confidence: 99%

“…Taking the advantages of high exploration and exploitation process from GABC and Best-So-Far algorithm, a new hybrid method is proposed called a Guided Best ABC algorithm. The proposed GBABC algorithm will increase the effectiveness of typical ABC, GABC and Best-So-Far ABC algorithm [46], [62], [71], [72]. The GBABC will first use the gbest strategy through employing bee to increase the exploitation process according to the given problem, while the onlookers and scout bees will adapt the Best-So-Far method for exploration process with balance quantity.…”

Section: Proposed Gbest Guided Best-so-far Abc Algorithmmentioning

confidence: 99%

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Prediction of Crude Oil Prices using Hybrid Guided Best-So-Far Honey Bees Algorithm-Neural Networks

Tairan¹,

Shah²,

Aleryani³

2019

IJACSA

Self Cite

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The objective of this paper is the use of new hybrid meta-heuristic method called Guided Best-So-Far Honey Bees Inspired Algorithm with Artificial Neural Network (ANN) on the Prediction of Crude Oil Prices of Kingdom of Saudi Arabia (KSA). Very high volatility of crude oil prices is one of the main hurdles for the economic development; therefore, it's the need of the hour to predict crude oil prices, especially for oil-rich countries such as KSA. Hence, in this paper, we are proposing a hybrid algorithm, named: Guided Best-So-Far Artificial Bee Colony (GBABC) algorithm. The proposed algorithm has been trained and tested with ANN for finding the optimal weight values to increase the exploration and exploitation process with balance quantities to obtain the accurate prediction of crude oil prices. The KSA crude oil prices of the five years 2013 to 2017 have been used to train ANN with different topologies and learning parameters of the proposed method for the prediction of the crude oil prices of the next day. The simulation results have been very promising and encouraging of the proposed algorithm when compared and analyzed with ABC, GABC (Gbest Guided ABC) and Best-So-Far ABC methods for prediction purpose. In most cases, the actual prices and predicted crude oil KSA prices are very close, which were obtained by the proposed GBABC method based on the optimal weight values of ANN and minimum prediction error.

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“…They contain many important and valuable information, useful for complex system modeling and prediction. Time series data mining is an important means of the control and decision-making of practical problems in various fields [1,2]. In recent years, scholars have carried out a great amount of research work on chaotic time series analysis and prediction.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model Based on a Two-Layer Decomposition Approach and an Optimized Neural Network for Chaotic Time Series Prediction

Ren

2019

Symmetry

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The prediction of chaotic time series has been a popular research field in recent years. Due to the strong non-stationary and high complexity of the chaotic time series, it is difficult to directly analyze and predict depending on a single model, so the hybrid prediction model has become a promising and favorable alternative. In this paper, we put forward a novel hybrid model based on a two-layer decomposition approach and an optimized back propagation neural network (BPNN). The two-layer decomposition approach is proposed to obtain comprehensive information of the chaotic time series, which is composed of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD). The VMD algorithm is used for further decomposition of the high frequency subsequences obtained by CEEMDAN, after which the prediction performance is significantly improved. We then use the BPNN optimized by a firefly algorithm (FA) for prediction. The experimental results indicate that the two-layer decomposition approach is superior to other competing approaches in terms of four evaluation indexes in one-step and multi-step ahead predictions. The proposed hybrid model has a good prospect in the prediction of chaotic time series.

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A Quick Gbest Guided Artificial Bee Colony Algorithm for Stock Market Prices Prediction

Cited by 28 publications

References 34 publications

Groundwater Level Prediction for the Arid Oasis of Northwest China Based on the Artificial Bee Colony Algorithm and a Back-propagation Neural Network with Double Hidden Layers

Groundwater Level Prediction for the Arid Oasis of Northwest China Based on the Artificial Bee Colony Algorithm and a Back-propagation Neural Network with Double Hidden Layers

Prediction of Crude Oil Prices using Hybrid Guided Best-So-Far Honey Bees Algorithm-Neural Networks

A Hybrid Model Based on a Two-Layer Decomposition Approach and an Optimized Neural Network for Chaotic Time Series Prediction

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