Skip to Main Content
Traveling salesman problem (TSP) is a classic of difficult optimization problem. It is simple to describe, mathematically well characterized. But the actual best solution to TSP is computationally very hard, called a NP-complete problem. In this paper, continuous Hopfield network (CHN) is applied to solve TSP. The energy function to be minimized is determined both by constraints for a valid solution and by total length of touring path. Setting of parameters in energy function is crucial to the convergence and performance of the network. The role of each parameter is analyzed and criteria for choosing these parameters are described. Iterative computation algorithm of CHN is given. Computer simulation is conducted for 6-, City TSP. Some simulation results, such as convergence curve, iteration count, computation time, are used to evaluate this method.