A fast and reliable approach to TSP using positively self-feedbacked hopfield networks

In this paper, a fast and reliable approach to the Traveling Salesman Problem (TSP) using the positively self-feedbacked Hopfield neural networks is proposed. The Hopfield neural networks with positive self-feedbacks and its collective computational properties are studied. It is proved theoretically and confirmed by simulating the randomly generated Hopfield neural networks with positive self-feedbacks that the emergent collective properties of the original Hopfield neural networks also are present in this network, The network is applied to the TSP and results of computer simulations are presented and used to illustrate the computation power of the networks. The simulation results show that the Hopfield neural networks with positive self-feedbacks has a rate of success higher than the original Hopfield neural networks for solving the TSP, and converges faster to stable solution than the original Hopfield neural networks does.