Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The Traveling Salesman Problem (TSP) is a very hard optimization problem in the field of operations research. It has been shown to be NP-complete, and is an often-used benchmark for new optimization techniques. One of the main challenges with this problem is that standard, non-AI heuristic approaches such as the Lin-Kernighan algorithm (LK) and the chained LK variant are currently very effective and in wide use for the common fully connected, Euclidean variant that is considered here.  presents an algorithm that uses adaptive resonance theory (ART) in combination with a variation of the Lin-Kernighan local optimization algorithm to solve very large instances of the TSP. The primary advantage of this algorithm over traditional LK and chained-LK approaches is the increased scalability and parallelism allod by the divide-and-conquer clustering paradigm. Tours obtained by the algorithm are lor quality, but scaling is much better and there is a high potential for increasing performance using parallel hardware.
['Samuel A. Mulder', 'Donald C. Wunsch']
I am writing a research paper on a idea Million city traveling salesman problem solution by divide and conquer clustering with adaptive resonance neural networks
[{"from": "human", "value": "I am writing a research paper on a idea Million city traveling salesman problem solution by divide and conquer clustering with adaptive resonance neural networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The Traveling Salesman Problem (TSP) is a very hard optimization problem in the field of operations research. It has been shown to be NP-complete, and is an often-used benchmark for new optimization techniques. One of the main challenges with this problem is that standard, non-AI heuristic approaches such as the Lin-Kernighan algorithm (LK) and the chained LK variant are currently very effective and in wide use for the common fully connected, Euclidean variant that is considered here.  presents an algorithm that uses adaptive resonance theory (ART) in combination with a variation of the Lin-Kernighan local optimization algorithm to solve very large instances of the TSP. The primary advantage of this algorithm over traditional LK and chained-LK approaches is the increased scalability and parallelism allod by the divide-and-conquer clustering paradigm. Tours obtained by the algorithm are lor quality, but scaling is much better and there is a high potential for increasing performance using parallel hardware."}]