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The accumulated experience ant colony for the travelling salesman problem
Citation
Montgomery, J and Randall, M, The accumulated experience ant colony for the travelling salesman problem, International Journal of Computational Intelligence and Applications, 3, (2) pp. 189-198. ISSN 1469-0268 (2003) [Refereed Article]
Copyright Statement
c World Scientific Publishing Company
DOI: doi:10.1142/S1469026803000938
Abstract
Ant colony optimization techniques are usually guided by pheromone and heuristic cost information when choosing the next element to add to a solution. However, while an individual element may be attractive, usually its long term consequences are neither known nor considered. For instance, a short link in a traveling salesman problem may be incorporated into an ant's solution, yet, as a consequence of this link, the rest of the path may be longer than if another link was chosen. The Accumulated Experience Ant Colony uses the previous experiences of the colony to guide in the choice of elements. This is in addition to the normal pheromone and heuristic costs. Two versions of the algorithm are presented, the original and an improved AEAC that makes greater use of accumulated experience. The results indicate that the original algorithm finds improved solutions on problems with less than 100 cities, while the improved algorithm finds better solutions on larger problems.
Item Details
Item Type: | Refereed Article |
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Keywords: | ant colony optimisation, travelling salesman problem, optimisation |
Research Division: | Information and Computing Sciences |
Research Group: | Machine learning |
Research Field: | Neural networks |
Objective Division: | Expanding Knowledge |
Objective Group: | Expanding knowledge |
Objective Field: | Expanding knowledge in the information and computing sciences |
UTAS Author: | Montgomery, J (Dr James Montgomery) |
ID Code: | 97254 |
Year Published: | 2003 |
Deposited By: | Information and Communication Technology |
Deposited On: | 2014-12-09 |
Last Modified: | 2016-01-19 |
Downloads: | 0 |
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