Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Credits to Luca Di Gaspero for the suggestion.
References
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)
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)
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)
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)
R. De Landtsheer, C. Ponsard, Oscar.cbls: an open source framework for constraint-based local search, in Proceedings of ORBEL’27, 2013
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)
T. Frühwirth, L. Michel, C. Schulte, Constraints in procedural and concurrent languages, in Constraint Programming Handbook (Elsevier, Amsterdam, 2006), pp. 451–492
M. Gendreau, J.-Y. Potvin, Handbook of Metaheuristics (Springer, New York, 2010)
F.W. Glover, G.A. Kochenberger, Handbook of Metaheuristics. International Series in Operations Research & Management Science (Springer, Berlin, 2003)
Emilia GOLEMANOVA, Declarative implementations of search strategies for solving CSPs in control network programming. WSEAS Trans. Comput. 12(4), 174–183 (2013)
C. Groer, Parallel and serial algorithms for vehicle routing problems, in BiblioBazaar, 2011
H.H. Hoos, T. Stützle, Stochastic Local Search: Foundations and Applications (Morgan Kaufmann, Burlington, 2005)
L. Michel, P. Van Hentenryck, Localizer: a modeling language for local search, in Principles and Practice of Constraint Programming-CP97, 1997
L. Michel, P. Van Hentenryck, Iterative relaxations for iterative flattening in cumulative scheduling, in ICAPS, vol. 4, 2004, pp. 200–208
S. Mouthuy, P. Van Hentenryck, Y. Deville, Constraint-based very large-scale neighborhood search. Constraints 17(2), 87–122 (2012)
NICTA Optimization Research Group. Minizinc and flatzinc. http://www.minizinc.org/
OscaR Team, OscaR: Operational research in Scala, 2012. Available under the LGPL licence from https://bitbucket.org/oscarlib/oscar
C. Ponsard, R. De Landtsheer, Y. Guyot, A high-level, modular and declarative modeling framework for routing problems, in Proceedings of ORBEL’28, 2014
T. Schrijvers, G. Tack, P. Wuille, H. Samulowitz, P.J. Stuckey, Search combinators, in Principles and Practice of Constraint Programming (Springer, Berlin, 2011), pp. 774–788
The Scala programming language. http://www.scala-lang.org
S. Thevenin, N. Zufferey, M. Widmer, Metaheuristics for a scheduling problem with rejection and tardiness penalties. J. Sched. 18(1), 89–105 (2015)
P. Van Hentenryck, L. Michel, Control abstractions for local search. Constraints 10(2), 137–157 (2005)
P. Van Hentenryck, L. Michel, Constraint-Based Local Search (MIT Press, Cambridge, 2009)
P. Van Hentenryck, L. Michel, L. Liu, Constraint-based combinators for local search, in Principles and Practice of Constraint Programming, 2004
Acknowledgements
This research was conducted under the SimQRi research project (ERANET CORNET, grant nr 1318172).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
De Landtsheer, R., Guyot, Y., Ospina, G., Ponsard, C. (2018). Combining Neighborhoods into Local Search Strategies. In: Amodeo, L., Talbi, EG., Yalaoui, F. (eds) Recent Developments in Metaheuristics. Operations Research/Computer Science Interfaces Series, vol 62. Springer, Cham. https://doi.org/10.1007/978-3-319-58253-5_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-58253-5_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-58252-8
Online ISBN: 978-3-319-58253-5
eBook Packages: Business and ManagementBusiness and Management (R0)