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Authors: Manisha Israni and Shyam Sundar

Affiliation: National Institute of Technology Raipur, India

Keyword(s): Tree Spanner, Weighted Graph, Swarm Intelligence, Ant Colony Optimization

Abstract: A tree $t$-spanner of a given connected graph is a spanning tree $T$ in which the ratio of distance between every pair of vertices is at most $t$ times their distance in the graph, where $t$ is a parameter known as stretch factor of $T$. The tree $t$-spanner problem deals with finding a spanning tree in a connected graph whose stretch factor is minimum amongst all spanning trees of the graph. For unweighted graph, this problem is $\mathcal{NP}$-Hard for any fixed $t \geq 4$, whereas for weighted graph, this problem is $\mathcal{NP}$-Hard for any fixed ~ $t > 1$. This paper concerns this problem for connected, undirected, and weighted graph and proposes three variants of ant colony optimization (ACO) approach for this problem. ACO approach is a swarm intelligence technique inspired by the foraging behavior of real ants. All three variants of ACO approach have been tested on a set of randomly generated graph instances. Computational results show the effectiveness of all three variants of ACO approach. (More)

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Paper citation in several formats:
Israni, M. and Sundar, S. (2017). Ant Colony Optimization Approaches for the Tree t-Spanner Problem. In Proceedings of the 9th International Joint Conference on Computational Intelligence (IJCCI 2017) - IJCCI; ISBN 978-989-758-274-5; ISSN 2184-3236, SciTePress, pages 200-206. DOI: 10.5220/0006490002000206

@conference{ijcci17,
author={Manisha Israni. and Shyam Sundar.},
title={Ant Colony Optimization Approaches for the Tree t-Spanner Problem},
booktitle={Proceedings of the 9th International Joint Conference on Computational Intelligence (IJCCI 2017) - IJCCI},
year={2017},
pages={200-206},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006490002000206},
isbn={978-989-758-274-5},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Computational Intelligence (IJCCI 2017) - IJCCI
TI - Ant Colony Optimization Approaches for the Tree t-Spanner Problem
SN - 978-989-758-274-5
IS - 2184-3236
AU - Israni, M.
AU - Sundar, S.
PY - 2017
SP - 200
EP - 206
DO - 10.5220/0006490002000206
PB - SciTePress

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