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Artificial dragonfly algorithm in the Hopfield neural network for optimal Exact Boolean k satisfiability representation

Author

Listed:
  • Ghassan Ahmed Ali
  • Hamza Abubakar
  • Shehab Abdulhabib Saeed Alzaeemi
  • Abdulkarem H M Almawgani
  • Adel Sulaiman
  • Kim Gaik Tay

Abstract

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.

Suggested Citation

  • Ghassan Ahmed Ali & Hamza Abubakar & Shehab Abdulhabib Saeed Alzaeemi & Abdulkarem H M Almawgani & Adel Sulaiman & Kim Gaik Tay, 2023. "Artificial dragonfly algorithm in the Hopfield neural network for optimal Exact Boolean k satisfiability representation," PLOS ONE, Public Library of Science, vol. 18(9), pages 1-29, September.
    • Handle: RePEc:plo:pone00:0286874
    DOI: 10.1371/journal.pone.0286874
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    References listed on IDEAS

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    1. Bing Li & Yongkun Li, 2019. "Existence and Global Exponential Stability of Almost Automorphic Solution for Clifford-Valued High-Order Hopfield Neural Networks with Leakage Delays," Complexity, Hindawi, vol. 2019, pages 1-13, July.
    2. Gonçalo P. Matos & Luís M. Albino & Ricardo L. Saldanha & Ernesto M. Morgado, 2021. "Solving periodic timetabling problems with SAT and machine learning," Public Transport, Springer, vol. 13(3), pages 625-648, October.
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