Abstract
Logic programming is extensively used to describe relations and have both a declarative and an operational meaning. A 3-Satisfiability (3-SAT) program is a class of Boolean Satisfiability problem, where strictly 3 literals per clause are involved. There are various algorithms utilized to solve the 3-Satisfiability logic program. Exhaustive Search (ES) is the standard method, where the algorithm enumerates for every possible iteration to search for a solution. Imperialist Competitive (IC) algorithm, on the other hand, an evolutionary optimization method that is inspired by the imperialistic competition model. Applying the Imperialist Competitive algorithm to 3-Satisfiability logic programming shows its ability in dealing with different types of optimization problems. In this paper, the objective was to compare IC algorithm and ES algorithm in doing 3-SAT logic programming with Hopfield Neural Network (HNN). The performance of both models were evaluated by performance evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Global Minima Ratio and CPU Time. The expected outcome of the experiment is that the IC algorithm outperformed ES in 3-SAT logic programming.
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