| Summary: | Among the existing algorithms for TSP solution,the heuristic algorithm based on Iterated Local Search(ILS) performs the best,holding the world record on most of the public instances.The method for solution construction has a significant influence on the performance of ILS,and thus should be carefully designed.This paper proposes four different methods for solution construction,including a baseline algorithm that uses only static information such as the distances between cities to construct the initial solution,and three reinforcement-learning-based algorithms that attempt to utilize reinforcement learning to dig useful information from the historic information collected during the search for the construction of initial solutions.Experimental results on 25 public instances show that the reinforcement-learning-based methods using historic information can significantly improve the quality of the constructed solution as well as the performance of ILS.
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