Hybrid imperialistic competitive algorithm incorporated with hopfield neural network for robust 3 satisfiability logic programming

Vigneshwer Kathirvel (Universiti Sains Malaysia)
Mohd. Asyraf Mansor (Universiti Sains Malaysia)
Mohd Shareduwan Mohd Kasihmuddin (Universiti Sains Malaysia)
Saratha Sathasivam (Universiti Sains Malaysia)

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 8, No 2: June 2019

Imperialist Competitive algorithm (ICA) is a robust training algorithm inspired by the socio-politically motivated strategy. This paper focuses on utilizing a hybridized ICA with Hopfield Neural Network on a 3- Satisfiability (3-SAT) logic programming. Eventually the performance of the proposed algorithm will be compared to other 2 algorithms, which are HNN3SATES (ES) and HNN-3SATGA (GA). The performance shall be evaluated with the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Sum of Squares Error (SSE), Schwarz Bayesian Criterion (SBC), Global Minima Ratio and Computation Time (CPU time). The expected outcome will portray that the IC algorithm will outperform the other two algorithms in doing 3-SAT logic programming.