Combining Neighborhoods into Local Search Strategies

  • Renaud De LandtsheerEmail author
  • Yoann Guyot
  • Gustavo Ospina
  • Christophe Ponsard
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 62)


This paper presents a declarative framework for defining local search procedures. It proceeds by combining neighborhoods by means of so-called combinators that specify when neighborhoods should be explored, and introduce other aspects of the search procedures such as stop criteria, solution management, and various metaheuristics. Our approach introduces these higher-level concepts natively in local search frameworks in contrast with the current practice which still often relies on their ad-hoc implementation in imperative language. Our goal is to ease the development, understanding, experimentation, communication and maintenance of search procedures. This will also lead to better search procedures where lots of efficiency gains can be made both for optimality and speed. We provide a comprehensive overview of our framework along with a number of examples illustrating typical usage pattern and the ease of use of our framework. Our combinators are available in the search component of the OscaR.cbls solver.


Local Search Metaheuristics Search Strategies Neighborhoods Combinators 



This research was conducted under the SimQRi research project (ERANET CORNET, grant nr 1318172).


  1. 1.
    T. Benoist, B. Estellon, F. Gardi, R. Megel, K. Nouioua, LocalSolver 1.x: a black-box local-search solver for 0-1 programming. 4OR 9(3), 299–316 (2011)Google Scholar
  2. 2.
    G. Björdal, J.-N. Monette, P. Flener, J. Pearson, A constraint-based local search backend for MiniZinc, Constraints, J. Fast Track CP-AI-OR 20(3), 325–345 (2015)Google Scholar
  3. 3.
    S. Cahon, N. Melab, E.-G. Talbi, Paradiseo: a framework for the reusable design of parallel and distributed metaheuristics. J. Heuristics 10(3), 357–380 (2004)CrossRefGoogle Scholar
  4. 4.
    S. de Givry, L. Jeannin, A unified framework for partial and hybrid search methods in constraint programming. Comput. Oper. Res. 33(10), 2805–2833 (2006)CrossRefGoogle Scholar
  5. 5.
    R. De Landtsheer, C. Ponsard, Oscar.cbls: an open source framework for constraint-based local search, in Proceedings of ORBEL’27, 2013Google Scholar
  6. 6.
    L. Di Gaspero, A. Schaerf, Easylocal++: an object-oriented framework for the flexible design of local-search algorithms. Softw. Pract. Exp. 33(8), 733–765 (2003)CrossRefGoogle Scholar
  7. 7.
    T. Frühwirth, L. Michel, C. Schulte, Constraints in procedural and concurrent languages, in Constraint Programming Handbook (Elsevier, Amsterdam, 2006), pp. 451–492Google Scholar
  8. 8.
    M. Gendreau, J.-Y. Potvin, Handbook of Metaheuristics (Springer, New York, 2010)CrossRefGoogle Scholar
  9. 9.
    F.W. Glover, G.A. Kochenberger, Handbook of Metaheuristics. International Series in Operations Research & Management Science (Springer, Berlin, 2003)Google Scholar
  10. 10.
    Emilia GOLEMANOVA, Declarative implementations of search strategies for solving CSPs in control network programming. WSEAS Trans. Comput. 12(4), 174–183 (2013)Google Scholar
  11. 11.
    C. Groer, Parallel and serial algorithms for vehicle routing problems, in BiblioBazaar, 2011Google Scholar
  12. 12.
    H.H. Hoos, T. Stützle, Stochastic Local Search: Foundations and Applications (Morgan Kaufmann, Burlington, 2005)Google Scholar
  13. 13.
    L. Michel, P. Van Hentenryck, Localizer: a modeling language for local search, in Principles and Practice of Constraint Programming-CP97, 1997Google Scholar
  14. 14.
    L. Michel, P. Van Hentenryck, Iterative relaxations for iterative flattening in cumulative scheduling, in ICAPS, vol. 4, 2004, pp. 200–208Google Scholar
  15. 15.
    S. Mouthuy, P. Van Hentenryck, Y. Deville, Constraint-based very large-scale neighborhood search. Constraints 17(2), 87–122 (2012)CrossRefGoogle Scholar
  16. 16.
    NICTA Optimization Research Group. Minizinc and flatzinc.
  17. 17.
    OscaR Team, OscaR: Operational research in Scala, 2012. Available under the LGPL licence from
  18. 18.
    C. Ponsard, R. De Landtsheer, Y. Guyot, A high-level, modular and declarative modeling framework for routing problems, in Proceedings of ORBEL’28, 2014Google Scholar
  19. 19.
    T. Schrijvers, G. Tack, P. Wuille, H. Samulowitz, P.J. Stuckey, Search combinators, in Principles and Practice of Constraint Programming (Springer, Berlin, 2011), pp. 774–788Google Scholar
  20. 20.
    The Scala programming language.
  21. 21.
    S. Thevenin, N. Zufferey, M. Widmer, Metaheuristics for a scheduling problem with rejection and tardiness penalties. J. Sched. 18(1), 89–105 (2015)CrossRefGoogle Scholar
  22. 22.
    P. Van Hentenryck, L. Michel, Control abstractions for local search. Constraints 10(2), 137–157 (2005)CrossRefGoogle Scholar
  23. 23.
    P. Van Hentenryck, L. Michel, Constraint-Based Local Search (MIT Press, Cambridge, 2009)Google Scholar
  24. 24.
    P. Van Hentenryck, L. Michel, L. Liu, Constraint-based combinators for local search, in Principles and Practice of Constraint Programming, 2004Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Renaud De Landtsheer
    • 1
    Email author
  • Yoann Guyot
    • 1
  • Gustavo Ospina
    • 1
  • Christophe Ponsard
    • 1
  1. 1.CETIC Research CentreCharleroiBelgium

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