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Simulated annealing based on LabVIEW simulation for solving traveling salesman problem

Xie Ling

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Abstract

For the NP-complete hard problem which is easy to be described,but hard to be solved and the possible amounts of path increase exponentially with the amounts of city in Traveling Salesman Problem,both resulting TSP is difficult to solve,this paper uses Simulated Annealing based on LabVIEW simulation to solve the problem for the first time.LabVIEW simulation results show that its unique array algorithms can effectively implement the Simulated annealing for TSP.Compared to other methods,this method is more simple,more practical and more precise.In addition,it has higher speed and is suitable for the TSP with any number of cities.

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What this paper is about

For the NP-complete hard problem which is easy to be described,but hard to be solved and the possible amounts of path increase exponentially with the amounts of city in Traveling Salesman Problem,both resulting TSP is difficult to solve,this paper uses Simulated Annealing based on LabVIEW simulation to solve the problem for the first time.LabVIEW simulation results show that its unique array algorithms can effectively implement the Simulated annealing for TSP.Compared to other methods,this method is more simple,more practical and more precise.In addition,it has higher speed and is suitable for the TSP with any number of cities.

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Available abstract

For the NP-complete hard problem which is easy to be described,but hard to be solved and the possible amounts of path increase exponentially with the amounts of city in Traveling Salesman Problem,both resulting TSP is difficult to solve,this paper uses Simulated Annealing based on LabVIEW simulation to solve the problem for the first time.LabVIEW simulation results show that its unique array algorithms can effectively implement the Simulated annealing for TSP.Compared to other methods,this method is more simple,more practical and more precise.In addition,it has higher speed and is suitable for the TSP with any number of cities.

Key concepts: Travelling salesman problem, Simulated annealing, Computer science, Mathematical optimization, Adaptive simulated annealing, Path (computing), 2-opt, Simple (philosophy)

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