Understanding the pheromone system within ant colony optimization

Authors:
Stephen Gilmour;Mark Dras
Affiliations:
Department of Computing, Macquarie University, North Ryde, Australia;Department of Computing, Macquarie University, North Ryde, Australia
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

Citing 2
Cited 2

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Kernelization as heuristic structure for the vertex cover problem

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Abstract

Ant Colony Optimization (ACO) is a collection of metaheuristics inspired by foraging in ant colonies, whose aim is to solve combinatorial optimization problems. We identify some principles behind the metaheuristics’ rules; and we show that ensuring their application, as a correction to a published algorithm for the vertex cover problem, leads to a statistically significant improvement in empirical results.