Robust Artificial Immune System in the Hopfield network for Maximum k-Satisfiability

• Main Authors: Mohd Asyraf Bin Mansor, Mohd Shareduwan Bin Mohd Kasihmuddin, Saratha Sathasivam
• Format: Article
• Language: English
• Published: Universidad Internacional de La Rioja (UNIR) 2017-08-01
• Series: International Journal of Interactive Multimedia and Artificial Intelligence
• Subjects: Algorithms; Artificial Immune System; Brute Force; Hopfield; Neural Network
• Online Access: http://www.ijimai.org/journal/node/1518

Artificial Immune System (AIS) algorithm is a novel and vibrant computational paradigm, enthused by the biological immune system. Over the last few years, the artificial immune system has been sprouting to solve numerous computational and combinatorial optimization problems. In this paper, we introduce the restricted MAX-kSAT as a constraint optimization problem that can be solved by a robust computational technique. Hence, we will implement the artificial immune system algorithm incorporated with the Hopfield neural network to solve the restricted MAX-kSAT problem. The proposed paradigm will be compared with the traditional method, Brute force search algorithm integrated with Hopfield neural network. The results demonstrate that the artificial immune system integrated with Hopfield network outperforms the conventional Hopfield network in solving restricted MAX-kSAT. All in all, the result has provided a concrete evidence of the effectiveness of our proposed paradigm to be applied in other constraint optimization problem. The work presented here has many profound implications for future studies to counter the variety of satisfiability problem.