Research on Survey Path Planning Based on mTSP Planning Model and Ant Algorithm

TSP is one of the most famous problems in graph theory, and it is often used in the fields of urban infrastructure planning, logistics distribution, and transportation route arrangement. So far, no effective algorithm has been found to deal with this type of problem. Scholars believe that large-scale examples of this type of problem cannot be solved with an accurate algorithm, and an effective approximate algorithm for this type of problem must be sought. In order to gain a deeper understanding of the mTSP problem, this paper takes the national survey route planning as an actual case, and combines the improved circle algorithm and the Ant Algorithm to propose a specific solution. Based on the large amount of real data collected, the research route planning of the research team of Beijing M University traversing 30 ethnic minority autonomous prefectures and 120 ethnic minority autonomous counties was used as a calculation case, and various constraints were comprehensively considered to construct a cluster center based on 18 clusters. The TSP planning model with time constraints splits provinces based on clustering results, regenerates the split provinces and neighboring provinces into a new improvement circle for optimization, and finally obtains a survey time of at least 9.5 years, and integrates a specific survey route.