Advertisement

Integrating ACO and Constraint Propagation

  • Bernd Meyer
  • Andreas Ernst
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3172)

Abstract

Ant Colony Optimisation algorithms perform competitively with other meta-heuristics for many types of optimisation problems, but unfortunately their performance does not always degrade gracefully when the problem contains hard constraints. Many industrially relevant problems, such as fleet routing, rostering and timetabling, are typically subject to hard constraints. A complementary technique for solving combinatorial optimisation problems is Constraint Programming (CP). CP techniques are specialized for solving hard constraints, but they may be inefficient as an optimisation method if the feasible space is very large. A hybrid approach combining both techniques therefore holds the hope to combine these complementary advantages. The paper explores how such an integration can be achieved and presents a hybrid search method CPACS derived by embedding CP into ACS. We have tested CPACS on job scheduling problems. Initial benchmark results are encouraging and suggest that CPACS has the biggest advantage over the individual methods for problems of medium tightness, where the constraints cause a highly fragmented but still very large search space.

Keywords

Feasible Solution Constraint Programming Constraint Propagation Hard Constraint Constraint Solver 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ascheuer, N., Fischetti, M., Grötschel, M.: Solving the asymmetric travelling salesman problem with time windows by branch-and-cut. Mathematical Programming 90(3), 475–506 (2001)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Barnier, N., Brisset, P.: Combine & conquer: Genetic algorithm and CP for optimization. In: Principles and Practice of Constraint Programming, Pisa (October 1998)Google Scholar
  3. 3.
    Bauer, A., Bullnheimer, B., Hartl, R.F., Strauss, C.: An ant colony optimization approach for the single machine total tardiness problem. In: Proceedings of the Congress on Evolutionary Computation, Washington/DC (July 1999)Google Scholar
  4. 4.
    Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys 35(3), 268–308 (2003)CrossRefGoogle Scholar
  5. 5.
    Blum, C., Sampels, M.: When model bias is stronger than selection pressure. In: Parallel Problem Solving From Nature (PPSN-VII), Granada (September 2002)Google Scholar
  6. 6.
    Carlsson, M., Ottosson, G., Carlson, B.: An open-ended finite domain constraint solver. In: Proc. PLILP 1997 Programming Languages: Implementations, Logics, and Programs, Southampton (September 1997)Google Scholar
  7. 7.
    Coello, C.A.: Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer Methods in Applied Mechanics and Engineering 191(11-12), 1245–1287 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Cordon, O., Herrera, F., Stützle, T.: A review on the ant colony optimization metaheuristic: Basis, models and new trends. Mathware and Soft Computing 9(2-3), 141–175 (2002)zbMATHMathSciNetGoogle Scholar
  9. 9.
    den Besten, M., Stützle, T., Dorigo, M.: Ant colony optimization for the total weighted tardiness problem. In: Parallel Problem Solving from Nature - PPSN VI, Paris, France (September 2000)Google Scholar
  10. 10.
    Dorigo, M., Di Caro, G., Gambardella, L.M.: Ant algorithms for discrete optimization. Artificial Life 5, 137–172 (1999)CrossRefGoogle Scholar
  11. 11.
    Dorigo, M., Gambardella, L.M.: Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997)CrossRefGoogle Scholar
  12. 12.
    Focacci, F., Laburthe, F., Lodi, A.: Local search and constraint programming. In: Glover, F., Kochenberger, G. (eds.) Handbook of metaheuristics, Kluwer, Boston (2003)Google Scholar
  13. 13.
    Lawler, E.L., Lenstra, J.K., Rinnooy Kan, A.H.G., Shmoys, D.B.: Sequencing and scheduling: algorithms and complexity. In: Graves, S.C., Rinnooy Kan, A.H.G., Zipkin, P.H. (eds.) Logistics of Production and Inventory, pp. 445–522. North Holland, Amsterdam (1993)CrossRefGoogle Scholar
  14. 14.
    Marriott, K., Stuckey, P.: Programming With Constraints. MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  15. 15.
    Pesant, G., Gendreau, M.: A constraint programming framework for local search methods. Journal of Heuristics 5(3), 255–279 (1999)zbMATHCrossRefGoogle Scholar
  16. 16.
    Pesant, G., Gendreau, M., Potvinand, J.-Y., Rousseau, J.-M.: An exact constraint logic programming algorithm for the traveling salesman problem with time windows. Transportation Science 32(1), 12–29 (1998)zbMATHCrossRefGoogle Scholar
  17. 17.
    Socha, K.: MAX-MIN ant system for international timetabling competition. Technical report, Universite Libre de Bruxelles (September 2003) TR/IRIDIA/2003-30Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Bernd Meyer
    • 1
    • 2
  • Andreas Ernst
    • 2
  1. 1.Monash University 
  2. 2.CSIROClaytonAustralia

Personalised recommendations