Solving TSP using Lotka-Volterra neural networks without self-excitatory
Manli Li, Jiali Yu, Stones Lei Zhang, Hong Qu · 2008
This paper proposes a new approach to solve Traveling Salesman Problems (TSPs) by using a class of Lotka-Volterra neural networks (LVNN) without self-excitatory. Some stability criteria that ensure the convergence of valid solutions are obtained. It is proved that a class of equilibrium states are stable if and only if they correspond to the valid solutions of the TSPs. That is, one can always obtain a valid solution whenever the network convergence to a stable state. A set of analytical conditions for optimal settings of LVNN is derived. The simulation results illustrate the theoretical analysis.