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Artificial dragonfly algorithm in the Hopfield neural network for optimal Exact Boolean k satisfiability representation.
Ali, Ghassan Ahmed; Abubakar, Hamza; Alzaeemi, Shehab Abdulhabib Saeed; Almawgani, Abdulkarem H M; Sulaiman, Adel; Tay, Kim Gaik.
Afiliación
  • Ali GA; College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia.
  • Abubakar H; Department of Mathematics, Isa Kaita College of Education, Dutsin-Ma, Katsina State, Nigeria.
  • Alzaeemi SAS; Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia.
  • Almawgani AHM; Electrical Engineering Department, College of Engineering, Najran University, Najran, Kingdom of Saudi Arabia.
  • Sulaiman A; College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia.
  • Tay KG; Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia.
PLoS One ; 18(9): e0286874, 2023.
Article en En | MEDLINE | ID: mdl-37747876
This study proposes a novel hybrid computational approach that integrates the artificial dragonfly algorithm (ADA) with the Hopfield neural network (HNN) to achieve an optimal representation of the Exact Boolean kSatisfiability (EBkSAT) logical rule. The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EBkSAT logic representation. To assess the performance of the proposed hybrid computational model, a specific Exact Boolean kSatisfiability problem is constructed, and simulated data sets are generated. The evaluation metrics employed include the global minimum ratio (GmR), root mean square error (RMSE), mean absolute percentage error (MAPE), and network computational time (CT) for EBkSAT representation. Comparative analyses are conducted between the results obtained from the proposed model and existing models in the literature. The findings demonstrate that the proposed hybrid model, ADA-HNN-EBkSAT, surpasses existing models in terms of accuracy and computational time. This suggests that the ADA algorithm exhibits effective compatibility with the HNN for achieving an optimal representation of the EBkSAT logical rule. These outcomes carry significant implications for addressing intricate optimization problems across diverse domains, including computer science, engineering, and business.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Redes Neurales de la Computación Tipo de estudio: Prognostic_studies Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2023 Tipo del documento: Article País de afiliación: Arabia Saudita Pais de publicación: Estados Unidos
Texto completo
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XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Redes Neurales de la Computación Tipo de estudio: Prognostic_studies Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2023 Tipo del documento: Article País de afiliación: Arabia Saudita Pais de publicación: Estados Unidos