Agent-based Modeling in doing Logic Programming in Fuzzy Hopfield Neural Network

This paper introduces a new approach to enhance performance in performing logic programming in the Hopfield neural network by using agent-based modeling. Hopfield networks have been broadly utilized to solve problems of combinatorial optimization. However, this network yielded a satisfiability problem because the network has grown larger, and it is more complex. Therefore, an improved algorithm has been proposed to enhance the Hopfield network’s capability by using the technique of fuzzy logic to provide more efficient energy relaxation and to avoid the local minimum solutions. Agent-based modeling has been introduced in this paper to conduct computer simulations, which aim at verifying and validating the introduced approach. By applying the technique of fuzzy Hopfield neural network clustering in the system, better quality solutions are produced, and the network is handled better despite the increasing complexity. Also, the solutions converged faster by the system. Accordingly, this technique of the fuzzy Hopfield neural network clustering in the system has produced better-quality solutions.