Dataset Information

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

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.

SUBMITTER: Ali GA

PROVIDER: S-EPMC10519610 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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

Ali Ghassan Ahmed GA Abubakar Hamza H Alzaeemi Shehab Abdulhabib Saeed SAS Almawgani Abdulkarem H M AHM Sulaiman Adel A Tay Kim Gaik KG

PloS one 20230925 9

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 computatio ...[more]

PMID: 37747876

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Project description:BackgroundSolutions to stochastic Boolean models are usually estimated by Monte Carlo simulations, but as the state space of these models can be enormous, there is an inherent uncertainty about the accuracy of Monte Carlo estimates and whether simulations have reached all attractors. Moreover, these models have timescale parameters (transition rates) that the probability values of stationary solutions depend on in complex ways, raising the necessity of parameter sensitivity analysis. We address these two issues by an exact calculation method for this class of models.ResultsWe show that the stationary probability values of the attractors of stochastic (asynchronous) continuous time Boolean models can be exactly calculated. The calculation does not require Monte Carlo simulations, instead it uses graph theoretical and matrix calculation methods previously applied in the context of chemical kinetics. In this version of the asynchronous updating framework the states of a logical model define a continuous time Markov chain and for a given initial condition the stationary solution is fully defined by the right and left nullspace of the master equation's kinetic matrix. We use topological sorting of the state transition graph and the dependencies between the nullspaces and the kinetic matrix to derive the stationary solution without simulations. We apply this calculation to several published Boolean models to analyze the under-explored question of the effect of transition rates on the stationary solutions and show they can be sensitive to parameter changes. The analysis distinguishes processes robust or, alternatively, sensitive to parameter values, providing both methodological and biological insights.ConclusionUp to an intermediate size (the biggest model analyzed is 23 nodes) stochastic Boolean models can be efficiently solved by an exact matrix method, without using Monte Carlo simulations. Sensitivity analysis with respect to the model's timescale parameters often reveals a small subset of all parameters that primarily determine the stationary probability of attractor states.

Dataset Information

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

Publications

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

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