T-ACO Tournament Ant Colony Optimisation for High-dimensional Problems

Emmanuel Sapin, Ed Keedwell

Abstract

Standard ACO implementations use a roulette wheel to allow ants to make path decisions at each node of the topology which works well for problems of smaller dimensionality, but breaks down when higher numbers of variables are considered. Such problems are becoming commonplace in biology and particularly in genomics where thousands of variables are considered in parallel. In this paper, a tournament-based ACO approach is proposed that is shown to outperform the roulette wheel-based approach for all problems of higher dimensionality in terms of the performance of the final solutions and execution time on problems taken from the literature.

References

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Paper Citation


in Harvard Style

Sapin E. and Keedwell E. (2012). T-ACO Tournament Ant Colony Optimisation for High-dimensional Problems . In Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2012) ISBN 978-989-8565-33-4, pages 81-86. DOI: 10.5220/0004159900810086


in Bibtex Style

@conference{ecta12,
author={Emmanuel Sapin and Ed Keedwell},
title={T-ACO Tournament Ant Colony Optimisation for High-dimensional Problems},
booktitle={Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2012)},
year={2012},
pages={81-86},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004159900810086},
isbn={978-989-8565-33-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2012)
TI - T-ACO Tournament Ant Colony Optimisation for High-dimensional Problems
SN - 978-989-8565-33-4
AU - Sapin E.
AU - Keedwell E.
PY - 2012
SP - 81
EP - 86
DO - 10.5220/0004159900810086