A Shortest Path Routing Algorithm Based on Chaotic Neural Networks

With the rapid development of computer networks, routing algorithms are required to achieve higher response speeds. As a new computational tool for solving combinational optimization problems, neural networks attract many attentions in the application for network routing. Compared with traditional algorithms adopting serial way, neural networks may become a powerful competitor in this field, relying on the parallel architecture and hardware approach. A shortest path routing algorithm based on chaotic neural networks is presented. Simulation results show that the algorithm can efficiently avoid being trapped in a local minimum compared with the Hopfield model, and has obvious improvement in convergence speed.