• 제목/요약/키워드: Kruskal 알고리즘

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An Efficient Implementation of Kruskal's and Reverse-Delete Minimum Spanning Tree Algorithm (Kruskal과 역-삭제 최소신장트리 알고리즘의 효율적 구현 방법)

  • Choi, Myeong-Bok;Lee, Sang-Un
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.13 no.2
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    • pp.103-114
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    • 2013
  • This paper suggests a method to reduce the number of performances of Kruskal and Reverse-delete algorithms. Present Kruskal and Reverse-delete algorithms verify whether the cycle occurs within the edges of the graph. For this reason, they have problems of unnecessarily performing extra algorithms from the edges, even though they've already obtained the minimum spanning tree. This paper, first of all, suggests the 1st method which reduces the no. of performances by introducing stop point criteria of algorithm, but at the same time, performs algorithms from all the edges, just like how Kruskal and Reverse-delete algorithms. Next, it suggests the 2nd method which finds the minimum spanning tree from the remaining edges after getting rid of all the unnecessary edges which are considered not to affect the minimum spanning tree. These suggested methods have an effect of terminating algorithm at least 1.4 times and at most 3.86times than Kruskal and Reverse-delete algorithms, when applied to the real graphs. We have found that the 2nd method of the Reverse-delete algorithm has the fastest speed in terminating an algorithm, among 4 algorithms which are results of the 2 suggested methods being applied to 2 algorithms.

Hybrid Minimum Spanning Tree Algorithm (하이브리드 최소신장트리 알고리즘)

  • Lee, Sang-Un
    • The KIPS Transactions:PartA
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    • v.17A no.3
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    • pp.159-166
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    • 2010
  • In this paper, to obtain the Minimum Spanning Tree (MST) from the graph with several nodes having the same weight, I applied both Bor$\dot{u}$vka and Kruskal MST algorithms. The result came out to such a way that Kruskal MST algorithm succeeded to obtain MST, but not did the Prim MST algorithm. It is also found that an algorithm that chooses Inter-MSF MWE in the $2^{nd}$ stage of Bor$\dot{u}$vka is quite complicating. The $1^{st}$ stage of Bor$\dot{u}$vka has an advantage of obtaining Minimum Spanning Forest (MSF) with the least number of the edges, and on the other hand, Kruskal MST algorithm has an advantage of always obtaining MST though it deals with all the edges. Therefore, this paper suggests an Hybrid MST algorithm which consists of the merits of both Bor$\dot{u}$vka's $1^{st}$ stage and Kruskal MST algorithm. When applied additionally to 6 graphs, Hybrid MST algorithm has a same effect as that of Kruskal MST algorithm. Also, comparing the algorithm performance speed and capacity, Hybrid MST algorithm has shown the greatest performance Therefore, the suggested algorithm can be used as the generalized MST algorithm.

Proposal of Minimum Spanning Tree Algorithm using 2-Edges Connected Grap (2-간선 연결 그래프를 사용한 최소신장트리 알고리즘 제안)

  • Lee, Sang-Un
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.14 no.4
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    • pp.233-241
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    • 2014
  • This paper suggests a fast minimum spanning tree algorithm which simplify the original graph to 2-edge connected graph, and using the cycling property. Borůvka algorithm firstly gets the partial spanning tree using cycle property for one-edge connected graph that selects the only one minimum weighted edge (e) per vertex (v). Additionally, that selects minimum weighted edge between partial spanning trees using cut property. Kruskal algorithm uses cut property for ascending ordered of all edges. Reverse-delete algorithm uses cycle property for descending ordered of all edges. Borůvka and Kruskal algorithms always perform |e| times for all edges. The proposed algorithm obtains 2-edge connected graph that selects 2 minimum weighted edges for each vertex firstly. Secondly, we use cycle property for 2-edges connected graph, and stop the algorithm until |e|=|v|-1 For actual 10 benchmark data, The proposed algorithm can be get the minimum spanning trees. Also, this algorithm reduces 60% of the trial number than Borůvka, Kruskal and Reverse-delete algorithms.

Generalized Borůvka's Minimum Spanning Tree Algorithm (일반화된 Borůvka 최소신장트리 알고리즘)

  • Choi, Myeong-Bok;Lee, Sang-Un
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.12 no.6
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    • pp.165-173
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    • 2012
  • Given a connected, weighted, and undirected graph, the Minimum Spanning Tree (MST) should have minimum sum of weights, connected all vertices, and without any cycle taking place. Borůvka Algorithm is firstly suggested as an algorithm to evaluate the MST, but it is not widely used rather than Prim and Kruskal algorithms. Borůvka algorithm selects the Minimum Weight Edge (MWE) from each vertex with distinct weights in $1^{st}$ stage, and selects the MWE from each MSF (Minimum Spanning Forest) in $2^{nd}$ stage. But the cycle check and the number of MSF in $1^{st}$ stage and $2^{nd}$ stage are difficult to implication by computer program even if it is easy to verify visually. This paper suggests the generalized Borůvka Algorithm, This algorithm selects all of the same MWEs for each vertex, then checks the cycle and constructs MSF for ascending sorted MWEs. Kruskal method bring into this process. if the number of MSF greats then 1, this algorithm selects MWE from ascending sorted inter-MSF edges. The generalized Borůvka algorithm is verified its application by being applied to the 7 graphs with the many minimum weights or distinct weight edges for any vertex. As a result, the generalized Borůvka algorithm is less required for cycle verification then the Kruskal algorithm. Therefore, the generalized Borůvka algorithm is more fast to obtain MST then Kruskal algorithm.

A Study on Distribution System Reconfiguration using GA and Kruskal Algorithm (유전 알고리즘과 Kruskal 알고리즘을 이용한 배전계통 재구성에 관한 연구)

  • An, Jin-O;Kim, Se-Ho
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.49 no.3
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    • pp.118-123
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    • 2000
  • This paper presents an efficient algorithm for loss reduction and load balancing by sectionalizing switch operation in large scale distribution system of radial type. We use Genetic algorithm and Kruskal algorithm to solve distribution system reconfiguration. Genetic algorithm is used to minimize objective function including loss and load balancing items. Kruskal algorithm is used to satisfy the radial condition of distribution system. The experimental results show that the proposed method has the ability to search a good solution regardless of initial configuration and size of system.

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Watershed Delineation Algorithm Using Kruskal's Algorithm and Triangulated Irregular Network (크루스칼 알고리즘과 불규칙 삼각망을 이용한 유역 추출 알고리즘)

  • Park Mee-Jeong;Heo Hyun;Kim Tae-Gon;Suh Kyo;Lee Jeong-Jae
    • Journal of The Korean Society of Agricultural Engineers
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    • v.48 no.4
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    • pp.3-12
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    • 2006
  • Watershed is the land area that contributes runoff to an outlet point. To delineate an watershed, watershed delineation using GIS that contains grid data structure is the most general method. Some researchers have studied to implement algorithms that revise the TIN topography since it is difficult to delineate watershed boundary more accurately. In this study kruskal's greedy algorithm and triangulated irregular network (TIN) were used to delineate a watershed. This method does not require a conversion from to DEM in grid and automatically obtain(generates) the oulet points. Delineation algorithm was tested in Geosan-gun, Chung-cheongbuk-do and get small watershed areas. Finally, kruskal's algorithm could operate more precisely with revision algorithm.

Fast Determination of Minimum Spanning Tree Based on Down-sizing Technique of Edges Population (간선 모집단 규모축소 기법을 적용한 빠른 최소신장트리 결정)

  • Lee, Sang-Un;Choi, Myeong-Bok
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.14 no.1
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    • pp.51-59
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    • 2014
  • This paper suggests a method of lessening number of a graph's edges population in order to rapidly obtain the minimum spanning tree. The present minimum spanning tree algorithm works on all the edges of the graph. However, the suggested algorithm reduces the edges population size by means of applying a method of deleting maximum weight edges in advance from vertices with more than 2 valencies. Next, it applies a stopping criterion which ideally terminates Borůvka, Prim, Kruskal and Reverse-Delete algorithms for reduced edges population. On applying the suggested algorithm to 9 graphs, it was able to minimize averagely 83% of the edges that do not become MST. In addition, comparing to the original graph, edges are turned out to be lessened 38% by Borůvka, 37% by Prim, 39% by Kruskal and 73% by Reverse-Delete algorithm, and thereby the minimum spanning tree is obtained promptly.

Minimum Spanning Tree with Select-and-Delete Algorithm (선택-삭제 최소신장트리 알고리즘)

  • Choi, Myeong-Bok;Lee, Sang-Un
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.13 no.4
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    • pp.107-116
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    • 2013
  • This algorithm suggests a method in which a minimum spanning tree can be obtained fast by reducing the number of an algorithm execution. The suggested algorithm performs a select-and-delete process. In the select process, firstly, it performs Borůvka's first stage for all the vertices of a graph. Then it re-performs Borůvka's first stage for specific vertices and reduces the population of the edges. In the delete process, it deletes the maximum weight edge if any cycle occurs between the 3 edges of the edges with the reduced population. After, among the remaining edges, applying the valency concept, it gets rid of maximum weight edges. Finally, it eliminates the maximum weight edges if a cycle happens among the vertices with a big valency. The select-and-delete algorithm was applied to 9 various graphs and was evaluated its applicability. The suggested select process is believed to be the vest way to choose the edges, since it showed that it chose less number of big edges from 6 graphs, and only from 3 graphs, comparing to the number of edges that is to be performed when using MST algorithm. When applied the delete process to Kruskal algorithm, the number of performances by Kruskal was less in 6 graphs, but 1 more in each 3 graph. Also, when using the suggested delete process, 1 graph performed only the 1st stage, 5 graphs till 2nd stage, and the remaining till 3rd stage. Finally, the select-and-delete algorithm showed its least number of performances among the MST algorithms.

A Degree-Constrained Minimum Spanning Tree Algorithm Using k-opt (k-opt를 적용한 차수 제약 최소신장트리 알고리즘)

  • Lee, Sang-Un
    • Journal of the Korea Society of Computer and Information
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    • v.20 no.5
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    • pp.31-39
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    • 2015
  • The degree-constrained minimum spanning tree (d-MST) problem is considered NP-complete for no exact solution-yielding polynomial algorithm has been proposed to. One thus has to resort to an heuristic approximate algorithm to obtain an optimal solution to this problem. This paper therefore presents a polynomial time algorithm which obtains an intial solution to the d-MST with the help of Kruskal's algorithm and performs k-opt on the initial solution obtained so as to derive the final optimal solution. When tested on 4 graphs, the algorithm has successfully obtained the optimal solutions.

An Efficient Implementation of Kruskal's Algorithm for A Minimum Spanning Tree (최소신장트리를 위한 크루스칼 알고리즘의 효율적인 구현)

  • Lee, Ju-Young
    • Journal of the Korea Society of Computer and Information
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    • v.19 no.7
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    • pp.131-140
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    • 2014
  • In this paper, we present an efficient implementation of Kruskal's algorithm to obtain a minimum spanning tree. The proposed method utilizes the union-find data structure, reducing the depth of the tree of the node set by making the nodes in the path to root be the child node of the root of combined tree. This method can reduce the depth of the tree by shortening the path to the root and lowering the level of the node. This is an efficient method because if the tree's depth reduces, it could shorten the time of finding the root of the tree to which the node belongs. The performance of the proposed method is evaluated through the graphs generated randomly. The results showed that the proposed method outperformed the conventional method in terms of the depth of the tree.