Yet Another Local Search Method for Constraint Solving

  • Philippe Codognet
  • Daniel Diaz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2264)


We propose a generic, domain-independent local search method called adaptive search for solving Constraint Satisfaction Problems (CSP). We design a new heuristics that takes advantage of the structure of the problem in terms of constraints and variables and can guide the search more precisely than a global cost function to optimize (such as for instance the number of violated constraints). We also use an adaptive memory in the spirit of Tabu Search in order to prevent stagnation in local minima and loops. This method is generic, can apply to a large class of constraints (e.g. linear and non-linear arithmetic constraints, symbolic constraints, etc) and naturally copes with over-constrained problems. Preliminary results on some classical CSP problems show very encouraging performances.


Local search constraint solving combinatorial optimization search algorithms 


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  1. 1.
    E. Aarts and J. Lenstra (Eds). Local Search in Combinatorial Optimization. Wiley, 1997.Google Scholar
  2. 2.
    A. Borning, B. Freeman-Benson and M. Wilson. Constraint Hierarchies. Lisp and Symbolic Computation, vol. 5 no. 3, 1992, pp 223–270.CrossRefGoogle Scholar
  3. 3.
    P. Codognet and D. Diaz. Compiling Constraint in clp(FD). Journal of Logic Programming, Vol. 27, No. 3, June 1996.Google Scholar
  4. 4.
    A. Davenport, E. Tsang, Z. Kangmin and C. Wang. GENET: a connectionist architecture for solving constraint satisfaction problems by iterative improvement. In proc. AAAI 94, AAAI Press, 1994.Google Scholar
  5. 5.
    D. Diaz and P. Codognet. The implementation of GNU Prolog. In proc. SAC’OO, 15th ACM Symposium on Applied Computing. Como, Italy, ACM Press 2000.Google Scholar
  6. 6.
    F. Glover and M. Laguna. Tabu Search, Kluwer Academic Publishers, 1997.Google Scholar
  7. 7.
    P. Galinier and J-K. Hao. A General Approach for Constraint Solving by Local Search. draft, 2001.Google Scholar
  8. 8.
    J-K. Hao, P. Galinier and M. Habib. Metaheuristiques pour l’optimisation combinatoire et l’affectation sous contraintes. Revue d’ntelligence Artificielle, vol.2 no. 13, 1999, pp 283–324.Google Scholar
  9. 9.
    W. Harvey and M. Ginsberg. Limited Discrepancy Search. In proc. IJCAI’95, 14th International Joint Conference on Artificial Intelligence, Montreal, Canada, 1995.Google Scholar
  10. 10.
    S. Lin. Computer solutions of the traveling salesman problem. Bell System Technical Journal, vol. 44 (1965), pp 2245–2269.zbMATHMathSciNetGoogle Scholar
  11. 11.
    S. Lin and B. Kerninghan. An effective heuristic algorithm for the travelingsalesman problem. Operations Research, vol. 21 (1973), pp 498–516.zbMATHMathSciNetCrossRefGoogle Scholar
  12. 12.
    Z. Michalewicz and D. Fogel. How to solve it: Modern Heuristics, Springer Verlag 2000.Google Scholar
  13. 13.
    L. Michel and P. Van Hentenryck. Localizer: a modeling language for local search. In proc. CP’97, 3rd International Conference on Principles and Practice of Constraint Programming, Linz, Austria, Springer Verlag 1997.Google Scholar
  14. 14.
    L. Michel and P. Van Hentenryck. Localizer. Constraints, vol. 5 no. 1&2, 2000.Google Scholar
  15. 15.
    L. Michel and P. Van Hentenryck. Localizer++: an open library for local search. Research Report, Brown University 2001.Google Scholar
  16. 16.
    S. Minton, M. Johnston, A. Philips and P. Laird. Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems. Artificial Intelligence, vol. 58, 1992, pp 161–205.zbMATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    K. Nonobe and T. Ibaraki. Atabu search approach to the constraint satisfaction problem as a general problem solver. European Journal of Operational Research, vol. 106, 1998, pp 599–623.zbMATHCrossRefGoogle Scholar
  18. 18.
    G. Pesant and M. Gendreau. A Constraint Programming Framework for Local Search Methods. Journal of Heuristics, vol. 5 no. 3, 1999, pp 255–279.zbMATHCrossRefGoogle Scholar
  19. 19.
    J-F. Puget. AC++ implementation of CLP. In proc. SPICIS’94, Singapore, 1994.Google Scholar
  20. 20.
    V. J. Rayward-Smith, I. H. Osman, C. R. Reeves, G. D. Smith. Modern Heuristic Search Methods. Wiley, 1996.Google Scholar
  21. 21.
    V. Saraswat, P. Van Hentenryck et al. Constraint Programming, ACM Computing Surveys, vol. 28 no. 4, December 1996.Google Scholar
  22. 22.
    B. Selman, H. Levesque and D. Mitchell. A new method for solving hard satisfiability problems. In proc. AAAI’92, AAAI Press 1992.Google Scholar
  23. 23.
    B. Selman, H. Kautz and B. Cohen. Noise strategies for improving local search. In proc. AAAI’94, AAAI Press 1994.Google Scholar
  24. 24.
    C. Solnon. Solving permutation problems by ant colony optimization. In proc. ECAI’2000, Berlin, Germany, Wiley, 2000.Google Scholar
  25. 25.
    C. Truchet, C. Agon and G. Assayag. Recherche Adaptative et Contraintes Musicales. In proc. JFPLC2001, Journées Francophones de Programmation Logique et Programmation par Contraintes, P. Codognet (Ed.), Hermes, 2001.Google Scholar
  26. 26.
    J. P. Walser. Integer Optimization by Local Search: A Domain-Independent Approach, LNAI 1637, Springer Verlag 1999.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Philippe Codognet
    • 1
  • Daniel Diaz
    • 2
  1. 1.LIP6University of Paris 6ParisFRANCE
  2. 2.CRIUniversity of Paris IParis Cedex 13FRANCE

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