Optimal representation to High Order Random Boolean kSatisability via Election Algorithm as Heuristic Search Approach in Hopeld Neural Networks

This study proposed a hybridization of higher-order Random Boolean kSatisfiability (RANkSAT) with the Hopfield neural network (HNN) as neuro-dynamical model designed to reflect knowledge efficiently. The learning process has undergone significant changes and improvements according various types optimization problems. However, HNN is associated some limitations which include storage capacity being easily trapped local minimum solution. Election algorithm (EA) improve phase for optimal representation in higher order. main source inspiration Algorithm its ability extend power rule political parties beyond their borders when seeking endorsement. purpose utilize EA accelerate random k Satisfiability representation. global minima ratio (mR) statistical error accumulations (SEA) during training were used evaluate performance. result this revealed that our EA-HNN-RANkSAT outperformed ABC-HNN-RANkSAT ES-HNN-RANkSAT models terms mR SEA.This will further be extended accommodate novel field Reverse analysis (RA) involves data mining techniques analyse real-life