Solving maximum cut problem using improved Hopfield neural network

The goal of the maximum cut problem is to partition the vertex set of an undirected graph into two parts in order to maximize the cardinality of the set of edges cut by the partition. The maximum cut problem has many important applications including the design of VLSI circuits and communication networks. Moreover, many optimization problems can be formulated in terms of finding the maximum cut in a network or a graph. In this paper, we propose an improved Hopfield neural network algorithm for efficiently solving the maximum cut problem. A large number of instances have been simulated. The simulation results show that the proposed algorithm is much better than previous works for solving the maximum cut problem in terms of the computation time and the solution quality.