Solving C-TSP with random neural network
Shuang Cong · Journal of Changchun Post and Telecommunication Institute · 2004
Based on the algorithm of the typical optimal problems-TSP(Traveling Salesman Problem) with DRNN(Dynamical Random Neural Network), simplifying the parameter in feedback equation, aiming at the problem of time-cost and path-search when solving the large scale TSP, a new project of dividing large scale cities into several sub zones is used to solve the China-TSP problem. The final result is compared with other neural networks in references now available. Random neural network is verified to have the advantage to solve TSP with more than 10 variables. The experiment shows that the final path distant of 31-city-TSP (15 112.7 km) is shorter than the ones of 5 known methods.