### Abstract

This paper gives an algorithm for finding the minimum weight tree having k edges in an edge weighted graph. The algorithm combines a search and optimization technique based on pheromone with a weight based greedy local optimization. Experimental results on a large set of problem instances show that this algorithm matches or surpasses other algorithms including an ant colony optimization algorithm, a tabu search algorithm, an evolutionary algorithm and a greedy-based algorithm on all but one of the 138 tested instances.

Original language | English (US) |
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Pages (from-to) | 36-47 |

Number of pages | 12 |

Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |

Volume | 3102 |

State | Published - Dec 1 2004 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Theoretical Computer Science
- Computer Science(all)

### Cite this

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*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)*, vol. 3102, pp. 36-47.

**Ant system for the k-cardinality tree problem.** / Bui, Thang N.; Sundarraj, Gnanasekaran.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Ant system for the k-cardinality tree problem

AU - Bui, Thang N.

AU - Sundarraj, Gnanasekaran

PY - 2004/12/1

Y1 - 2004/12/1

N2 - This paper gives an algorithm for finding the minimum weight tree having k edges in an edge weighted graph. The algorithm combines a search and optimization technique based on pheromone with a weight based greedy local optimization. Experimental results on a large set of problem instances show that this algorithm matches or surpasses other algorithms including an ant colony optimization algorithm, a tabu search algorithm, an evolutionary algorithm and a greedy-based algorithm on all but one of the 138 tested instances.

AB - This paper gives an algorithm for finding the minimum weight tree having k edges in an edge weighted graph. The algorithm combines a search and optimization technique based on pheromone with a weight based greedy local optimization. Experimental results on a large set of problem instances show that this algorithm matches or surpasses other algorithms including an ant colony optimization algorithm, a tabu search algorithm, an evolutionary algorithm and a greedy-based algorithm on all but one of the 138 tested instances.

UR - http://www.scopus.com/inward/record.url?scp=33750279119&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33750279119&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:33750279119

VL - 3102

SP - 36

EP - 47

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

ER -