Multi-objective Hybrid Election Algorithm For Random K Satisfiability In Discrete Hopfield Neural Network - Karim, Syed Anayet (2023)
In the current Artificial Neural Network research development, symbolic logical structure plays a vital role for describing the concept of intelligence. The existing Discrete Hopfield Neural Network with systematic Satisfiability logical structures failed to produce non-repetitive final neuron states which tends to local minima solutions. In this regard, this thesis proposed non-systematic Random k Satisfiability logic for 3 k  , where k generates maximum three types of logical combinations (k=1,3; k=2,3; k=1,2,3) to report the behaviours of higher-order multiple logical structures. To analyse the logical combinations of Random k Satisfiability, this thesis will conduct experimentations with several performance metrics. The analysis revealed that the k=2,3 combination of Random k Satisfiability has more consistent interpretation and global solutions compared to the other combinations. Moreover, the optimal performance of Random k Satisfiability logic can be achieved by applying an efficient algorithm during the training phase of Discrete Hopfield Neural Network. One of the major features of an efficient algorithm is to make a proper balance in the exploration and exploitation strategy. In this regard, this thesis proposed a hybridized algorithm named Hybrid Election Algorithm that can well maintain the exploration-exploitation strategy.