Hybrid Artificial Bee Colony Algorithm for Weighted C-Type Random 2 Satisfiability in Discrete Hopfield Neural Network

In the field of Artificial Intelligence (AI), the implementation of propositional satisfiability logic rules in Discrete Hopfield Neural Networks (DHNN) has increasingly attracted attention. However, few studies have focused on expanding the storage capacity of DHNN. To address this gap, we propose the Hybrid Binary Artificial Bee Colony (HBABC) algorithm to optimize the Weighted C-Type Random 2 Satisfiability logic rule. Experimental results demonstrate that HBABC can enhance storage capacity by achieving 100% satisfaction of clauses and more than 8.4 unduplicated solutions. The HBABC algorithm exhibits high efficiency, robustness, and stability, making it a promising candidate for further applications and modifications in logical mining approaches to solve real-world problems requiring random dynamics analysis.