Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The continuous Hopfield network (CHN) can be used to solve, among other combinatorial optimization problems, the traveling salesman problem (TSP). In order to improve the performance of this heuristic technique, a divide-and-conquer strategy based on two phases is proposed. The first phase involves linking cities with the most neighbors to define a set of chains of cities and, secondly, to join these with isolated cities to define the final tour. Both problems are solved by mapping the two TSPs onto their respective CHNs. The associated parameter-setting procedures are deduced; these procedures ensure the feasibility of the obtained tours, and the quality of the solutions is compared with the pure CHN approach using some traveling salesman problem library (TSPLIB) instances. By means of this strategy, solving TSP instances with sizes of up to 13,509 cities are allod with the computational resources  had available. Finally, the new divide-and-conquer procedure is improved by tuning the parameter which controls the first phase.
['Lucas García', 'Pedro M. Talaván', 'Javier Yáñez']
I am writing a research paper on a idea Improving the Hopfield model performance when applied to the traveling salesman problem
[{"from": "human", "value": "I am writing a research paper on a idea Improving the Hopfield model performance when applied to the traveling salesman problem"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The continuous Hopfield network (CHN) can be used to solve, among other combinatorial optimization problems, the traveling salesman problem (TSP). In order to improve the performance of this heuristic technique, a divide-and-conquer strategy based on two phases is proposed. The first phase involves linking cities with the most neighbors to define a set of chains of cities and, secondly, to join these with isolated cities to define the final tour. Both problems are solved by mapping the two TSPs onto their respective CHNs. The associated parameter-setting procedures are deduced; these procedures ensure the feasibility of the obtained tours, and the quality of the solutions is compared with the pure CHN approach using some traveling salesman problem library (TSPLIB) instances. By means of this strategy, solving TSP instances with sizes of up to 13,509 cities are allod with the computational resources  had available. Finally, the new divide-and-conquer procedure is improved by tuning the parameter which controls the first phase."}]