Dynamic neural network with heuristics

With the deterministic nature and the difficulty of scaling, Hopfield-style neural network is readily to converge to one of local minima in the course of energy function minimization, not to escape from those undesirable solutions. Many researchers, who want to find the global minimum of the traveling salesman problem (TSP), have introduced various approaches to solve such conditions including heuristics, genetic algorithms, hybrid algorithms of some methods, etc. We introduce a simple heuristic algorithm which embeds the classical local search heuristics into the optimization neural network. The proposed algorithm is characterized with the best neighbors selection, which is used in the dynamic scheduling and in ordering the update sequence of neurons, and with the decidability check which is used to guarantee the near-optimal solution. The proposed algorithm enhances both the convergence speed and the quality of solutions