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Abstract

The Boolean satisfiability problem is one of the most important decision problems in mathematicallogic and computational science for determining whether or not a solution to a Boolean formula exists. The Hopfieldneural network (HNN) is a major type of artificial neural network (ANN), and it is widely used to solve variousoptimization and decision problems due to its energy minimization mechanism. Existing models that incorporate astandalone network-projected non-versatile framework as a fundamental HNN employ random search in their trainingstages and are sometimes trapped at the local optimal solution. In this study, the ant colony optimization (ACO)algorithm as a novel variant of the probabilistic metaheuristic algorithm inspired by the behavior of real ants isincorporated into the training phase of HNN to accelerate the training process for random Boolean k satisfiabilityreverse analysis based on logic mining. The performance of the proposed hybrid model is evaluated in terms of therobustness and accuracy of the induced logic obtained by using the Agricultural Soil Fertility Data Set. Experimentalsimulation results reveal that ACO can effectively work with HNN for Random 3 satisfiability reverse analysis with87.5% classification accuracy

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