Ants Colony Optimization Algorithm in the Hopfield Neural Network for Agricultural Soil Fertility Reverse Analysis

The Boolean Satisfiability Problem (BSAT) is one of the most important decision problems in mathematical logic and computational sciences for determining whether or not a solution to formula.. Hopfield neural network (HNN) major type artificial (NN) popularly known it used solving various optimization based on its energy minimization machinism. existing models that incorporate standalone projected non-versatile framework as fundamental employs random search training stages sometimes get trapped at local optimal solution. In this study, Ants Colony Optimzation Algorithm (ACO) novel variant probabilistic metaheuristic algorithm (MA) inspired by behavior real Ants, has been incorporated phase types accelerate process Random kSatisfiability reverse analysis (RANkSATRA) mining. proposed hybrid model evaluated according robustness accuracy induced obtained agricultural soil fertility data set (ASFDS). Based experimental simulation results, reveals ACO can effectively work with 3 Reverse Analysis 87.5 % classification