A Specialized Transiently Chaotic Neural Network for the Minimum Vertex Cover Problem

The minimum vertex cover (MVC) problem is a classic graph optimization problem. It is well known that it is an NP-Complete problem. By analyzing the dynamic behavior of the transiently chaotic neural network and the characteristics of the minimum vertex cover problem, we propose a specialized transiently chaotic neural network for this problem. Extensive simulation results show that the transiently chaotic neural network can yield better solutions to random graphs than other existing algorithms for this problem, and yield 100% convergence rate to optimal solutions to some benchmark graphs. Moreover, the new model uses fewer steps to converge to saturation states in comparison with the original transiently chaotic neural network.