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The effectiveness of dynamic ant colony tuning

Published: 07 July 2007 Publication History

Abstract

We examine the Genetically Modified Ant Colony System (GMACS) algorithm [3], which claims to dynamically tune an Ant Colony Optimization (ACO) algorithm to its near-optimal parameters. While our research indicates that the use of GMACS does result in higher quality solutions over a hand-tuned ACO algorithm, we found that the algorithm is ultimately hindered by its emphasis on randomized ant breeding. Specifically, our investigation shows that tuning ACO parameters on a single colony using a genetic algorithm, as done by GMACS, is not as effective as it may first appear and has several drawbacks.

References

[1]
E. Bonabeau, M. Dorigo, and G. Theraulaz (1999). Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, New York.
[2]
M. Dorigo and L.M. Gambardella. Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem. IEEE Transactions on Evolutionary Computation, 1(1):53--66, 1997.
[3]
D. Gaertner and K. L. Clark. On Optimal Parameters for Ant Colony Optimization Algorithms. IC-AI 2005: 83--89.
[4]
TSP-Library (2001). http://www.iwr.uniheidelberg.de/groups/comopt/software/TSPLIB95/

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cover image ACM Conferences
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
July 2007
2313 pages
ISBN:9781595936974
DOI:10.1145/1276958

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2007

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  1. ant colony optimization
  2. genetic algorithms
  3. genetically modified ant colony system
  4. traveling salesman problem

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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