Candidate Set Strategies for Ant Colony Optimisation

  • Marcus Randall
  • James Montgomery
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2463)

Abstract

Ant Colony Optimisation based solvers systematically scan the set of possible solution elements before choosing a particular one. Hence, the computational time required for each step of the algorithm can be large. One way to overcome this is to limit the number of element choices to a sensible subset, or candidate set. This paper describes some novel generic candidate set strategies and tests these on the travelling salesman and car sequencing problems. The results show that the use of candidate sets helps to find competitive solutions to the test problems in a relatively short amount of time.

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References

  1. 1.
    Dorigo, M., Di Caro, G.: The Ant Colony Optimization Meta-heuristic. In Corne, D., Dorigo, M., Glover, F. (eds.): New Ideas in Optimization. McGraw-Hill, London (1999) 11–32Google Scholar
  2. 2.
    Dorigo, M., Gambardella, L.M.: Ant Colonies for the Traveling Salesman Problem. BioSystems 43 (1997) 73–81CrossRefGoogle Scholar
  3. 3.
    Stützle, T., Dorigo, M.: ACOAlgorithms for the Traveling Salesman Problem. In Miettinen, K., Makela, M., Neittaanmaki, P., Periaux, J. (eds.): Evolutionary Algorithms in Engineering and Computer Science. Wiley (1999)Google Scholar
  4. 4.
    Reinelt, G.: The Traveling Salesman: Computational Solutions for TSP Applications. Springer-Verlag, Berlin (1994)Google Scholar
  5. 5.
    Bullnheimer, B., Hartl, R.F., Strauß, C.: An Improved Ant System Algorithm for the Vehicle Routing Problem. Sixth Viennese workshop on Optimal Control, Dynamic Games, Nonlinear Dynamics and Adaptive Systems, Vienna, Austria (1997)Google Scholar
  6. 6.
    Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Boston (1997)MATHGoogle Scholar
  7. 7.
    Stützle, T., Hoos, H.: Improving the Ant System: A Detailed Report on the MAXMIN Ant System. Darmstadt University of Technology, Computer Science Department, Intellectics Group., Technical Report AIDA-96-12-Revised version (1996)Google Scholar
  8. 8.
    Smith, K., Palaniswami, M., Krishnamoorthy, M.: A Hybrid Neural Network Approach to Combinatorial Optimisation. Computers and Operations Research 73 (1996) 501–508MathSciNetGoogle Scholar
  9. 9.
    Randall, M., Montgomery, J.: Candidate Set Strategies for Ant Colony Optimisation. School of Information Technology, Bond University, Australia, Technical Report TR02-04 (2002)Google Scholar
  10. 10.
    Feo, T.A., Resende, M.G.C.: Greedy Randomized Adaptive Search Procedures. Journal of Global Optimization 6 (1995) 109–133MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Marcus Randall
    • 1
  • James Montgomery
    • 1
  1. 1.School of Information TechnologyBond University Gold CoastQueenslandAustralia

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