Acyclic shortest path searching method based on Dijkstra algorithm and Max-Min ant colony

Abstract

The invention discloses an acyclic shortest path searching method based on a Dijkstra algorithm and a Max-Min ant colony and mainly solves problems of high time complexity and non-optimal path weight existing in the prior art. The method comprises steps that 1), a weighted and directed graph G satisfying a path relationship is constructed, unnecessary nodes and directed edges are eliminated through pruning to acquire a weighted and directed graph G1 after pruning; 2), the weighted and directed graph G1 after pruning is simplified to acquire a simple graph G2 only containing source nodes, a must-pass node set and purpose nodes; and 3), the Dijkstra algorithm and the Max-Min ant colony method are both employed in the simple graph G2 to search an optimal path which is further outputted. Compared with the prior art, the method is advantaged in that a solution speed is fast, a shortest optimal path is acquired, and the method is applicable to path selection in a network routing system and line planning scenes for traffic transmission.

Description

technical field [0001] The invention belongs to the technical field of communication, and further relates to an acyclic shortest path search method, which can be used in path selection in a network routing system and route planning in transportation. Background technique [0002] The shortest path problem is to find the path with the minimum sum of path weights between the source node and the destination node in a given network graph. The classic shortest path methods include Dijkstra's method and Freud's method, but the original proposals of these two methods cannot be applied to the designated necessary nodes. At present, the methods for solving the shortest path through the designated necessary nodes mainly include depth-first traversal method, Dijkstra method, genetic method, and traditional ant colony method. Depth-first traversal is to find all the paths that meet the conditions, and compare the shortest path. Although the shortest path obtained is the optimal result,...

AI Technical Summary

Problems solved by technology

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

The disadvantage of this method is that only a necessary node sequence is given, the possibility of finding a solution is small and the weight of the found solution is usually large

[0007] Xu Qingzheng and Ke Xizheng proposed a genetic method to generate the acyclic shortest path in their paper "Research on the Problem Model of Necessary Point Shortest Path and the Corresponding Genetic Method" ("System Engineering and Electronic Technology" 2009), by constructing a unique fitness function , so that the chromosomes containing more necessary points can be preferentially selected into the next generation, but it is difficult to solve the network graph with a large number of nodes

[0008] In summary, the existing methods have high time complexity for solving large complex graphs with a large number of nodes, and the path weights obtained within the effective time are large, which is not suitable for real situations

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