Abstract
It’s a very popular issue regarding the minimum cost spanning tree which is of great practical and economical significance to solve it in a concise and accelerated way. In this paper, the basic ideas of Kruskal algorithm were discussed and then presented a new improved algorithm—two branch Kruskal algorithm, which is improved to choose a middle value. Finally, because the time complexity is reduced, and the process is more convenient, it is concluded that the improved Kruskal algorithm is more effective in most cases compared with the Kruskal algorithm.
Highlights
How to select the best path in many ways that cost the least is the problem what people often encounter
One wants to open a communication network or a pipeline, and how to design it to take it at least? The problem like this can be attributed to the problem of minimum cost spanning tree
//Kruskal algorithm for constructing minimum cost spanning tree //Input: weighted connected graph G= //Output: Et, edge collection from minimum cost spanning tree composed of G Ranking E in the order of non decreasing order of edge cost W (E) Et←Ø; encounter←0 //Initialize the vertex set of the tree and the size of the collection k←0
Summary
How to select the best path in many ways that cost the least is the problem what people often encounter. The problem like this can be attributed to the problem of minimum cost spanning tree. It was an important application of graphs in data structure. To obtain a spanning tree was to choose n − 1 edges from a cost undirected graph and this graph was still connected, and at the same time considered the minimum cost of the tree. Prim algorithm and Kruskal algorithm were classics in algorithms of minimum cost spanning tree [1] [2] [3] [4]. Is there a simpler way to find the minimum cost spanning tree? Is there a simpler way to find the minimum cost spanning tree? That’s the main content that we’re going to talk about
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