Satisfiability is a key symbolic language in Discrete Hopfield Neural Networks that contributes to the development of advanced Artificial Intelligence models. However, the absence of combined higher-orders satisfiability limits the full potential within the network. Therefore, this thesis introduces a optimizing discrete Hopfield neural networks using major random 1,3-satisfiability and hybrid artificial bee colony to evaluate the neuron behaviours within the network. The proposed logic demonstrates optimal performance compared to existing logical rules.