e-ISSN 2231-8542
ISSN 1511-3701

Home / Regular Issue / JTAS Vol. 28 (3) Jul. 2020 / JST-1878-2020


Modified Imperialistic Competitive Algorithm in Hopfield Neural Network for Boolean Three Satisfiability Logic Mining

Nur Ezlin Zamri, Alyaa Alway, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin and Saratha Sathasivam

Pertanika Journal of Tropical Agricultural Science, Volume 28, Issue 3, July 2020

Keywords: 3-satisfiability, Hopfield neural network, imperialist competitive algorithm, logic mining

Published on: 16 July 2020

Artificial neural networks (ANNs) are actively utilized by researchers due to their extensive capability during the training process of the networks. The intricate training stages of many ANNs provide a powerful mechanism in solving various optimization or classification tasks. The integration of an ANN with a robust training algorithm is the supreme model to outperform the existing framework. Therefore, this work presented the inclusion of three satisfiability Boolean logic in the Hopfield neural network (HNN) with a sturdy evolutionary algorithm inspired by the Imperialist Competitive Algorithm (ICA). In general, ICA stands out from other metaheuristics as it is inspired by the policy of extending the power and rule of a government/country beyond its own borders. Existing models that incorporate standalone HNN are projected as non-versatile frameworks as it fundamentally employs random search in its training stage. The main purpose of this work was to conduct a comprehensive comparison of the proposed model by using two real data sets with an elementary HNN with exhaustive search (ES) versus a HNN with a standard evolutionary algorithm, namely- the genetic algorithm (GA). The performance evaluation of the proposed model was analyzed by computing plausible errors, such as root mean square error (RMSE), mean absolute error (MAE), global minima ratio (Rm), computational time (CT) and accuracy (Q). The computational simulations were carried out by operating the different numbers of neurons in order to validate the efficiency of the proposed model in the training stage. Based on the simulations, the proposed model was found to execute the best performance in terms of attaining small errors and efficient computational time compared to other existing models.

ISSN 1511-3701

e-ISSN 2231-8542

Article ID


Download Full Article PDF

Share this article

Recent Articles