Searching method of shortest path passing by necessary peak points

Abstract

The invention discloses a search method for the shortest path passing through necessary vertices. The implementation steps are: (1) Construct a weighted directed graph that satisfies the path relationship; (2) Find the shortest path between the search starting point and each necessary vertex; (3) Determine whether the path exists; (4) If it exists , then use the necessary vertex as the new search starting point, and continue to search for the path; (5) if it does not exist, then go back to the previous necessary vertex, and select the next shortest path to continue searching; (6) search the last found necessary vertex to the destination The shortest path between vertices; (7) The path can be found by combining the shortest paths found between all necessary vertices. By comparing the present invention with the depth-first search method, the genetic method and the ant colony method through experiments, it can be found that the present invention has the advantages of fast solution speed, good stability and the like.

Description

technical field [0001] The invention belongs to the technical field of physics, and further relates to a search method for the shortest path passing through necessary vertices in the technical field of path search. The invention can solve a series of similar problems such as "traveler problem", "bus route design problem" and "military material transportation problem". Background technique [0002] The problem of the shortest path passing through the vertices is a kind of problem that has been widely valued and studied, and is widely used in many fields such as computer science, traffic engineering, communication engineering, system engineering, operations research, information theory, and control theory. In the existing methods, it is difficult to obtain the optimal path when the number of necessary vertices is large or the total number of vertices is large. Generally, the approximate solution of the optimal path is obtained by finding a plausible optimal path. The "travele...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that the genetic method has a lot of randomness in the actual solution, and it is easy to converge to the seemingly optimal path prematurely, which leads to the inability to find the optimal path stably.

The disadvantage of this method is that when common vertices are used to find the local shortest path between different segments, this method will easily lead to the inability to find the path

The disadvantage of this method is: in the process of finding the shortest path through the necessary vertices, some vertices may be repeated multiple times, which leads to the generation of loops

The shortcomings of this method are: firstly, it is not necessarily possible to find the shortest path according to the given order of necessary points, and secondly, it takes a very long time to find the path between all necessary vertices through permutation and combination. For large-scale data not practical

The shortcomings of this method are: firstly, the full permutation and combination is very time-consuming, resulting in slow solution speed, and secondly, when using n! combinations to get n! When there are two paths, a loop may occur

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