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International Conference on Principles and Practice of Constraint Programming

CP 2013: Principles and Practice of Constraint Programming pp 611–627Cite as

Multi-Objective Large Neighborhood Search

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Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 8124))

Abstract

Large neighborhood search (LNS) [25] is a framework that combines the expressiveness of constraint programming with the efficiency of local search to solve combinatorial optimization problems. This paper introduces an extension of LNS, called multi-objective LNS (MO-LNS), to solve multi-objective combinatorial optimization problems ubiquitous in practice. The idea of MO-LNS is to maintain a set of nondominated solutions rather than just one best-so-far solution. At each iteration, one of these solutions is selected, relaxed and optimized in order to strictly improve the hypervolume of the maintained set of nondominated solutions. We introduce modeling abstractions into the OscaR solver for MO-LNS and show experimentally the efficiency of this approach on various multi-objective combinatorial optimization problems.

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References

  1. Abraham, A., Jain, L.: Evolutionary multiobjective optimization. Evolutionary Multiobjective Optimization (2005)

    Google Scholar 

  2. Christopher Beck, J.: Solution-guided multi-point constructive search for job shop scheduling. J. Artif. Int. Res. 29(1), 49–77 (2007)

    Google Scholar 

  3. Bent, R., Hentenryck, P.V.: A two-stage hybrid algorithm for pickup and delivery vehicle routing problems with time windows. Computers & Operations Research 33(4), 875–893 (2006)

    Article  MATH  Google Scholar 

  4. Cahon, S., Melab, N., Talbi, E.-G.: Paradiseo: A framework for the reusable design of parallel and distributed metaheuristics. Journal of Heuristics 10(3), 357–380 (2004)

    Article  Google Scholar 

  5. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-2. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)

    Article  Google Scholar 

  6. Durillo, J.J., Nebro, A.J.: jmetal: A java framework for multi-objective optimization. Advances in Engineering Software 42, 760–771 (2011)

    Article  Google Scholar 

  7. Ehrgott, M.: Multicriteria optimization, vol. 2. Springer, Berlin (2005)

    MATH  Google Scholar 

  8. Ehrgott, M., Gandibleux, X.: Hybrid metaheuristics for multi-objective combinatorial optimization. In: Blum, C., Aguilera, M.J.B., Roli, A., Sampels, M. (eds.) Hybrid metaheuristics. SCI, vol. 114, pp. 221–259. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  9. Gavanelli, M.: An algorithm for multi-criteria optimization in csps. ECAI 2, 136–140 (2002)

    Google Scholar 

  10. Haimes, Y.Y., Lasdon, L.S., Wismer, D.A.: On a bicriterion formulation of the problems of integrated system identification and system optimization. IEEE Transactions on Systems, Man, and Cybernetics 1(3), 296–297 (1971)

    Article  MathSciNet  MATH  Google Scholar 

  11. Van, P., Van Hentenryck, P., Michel, L.: Nondeterministic control for hybrid search. Michel 11(4), 353–373 (2006)

    MATH  Google Scholar 

  12. Knowles, J.: Towards landscape analyses to inform the design of a hybrid local search for the multiobjective quadratic assignment problem. Soft Computing Systems: Design, Management and Applications 2002, 271–279 (2002)

    Google Scholar 

  13. Knowles, J., Corne, D.: Instance generators and test suites for the multiobjective quadratic assignment problem. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 295–310. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  14. Laborie, P., Godard, D.: Self-adapting large neighborhood search: Application to single-mode scheduling problems. In: Proceedings MISTA 2007, Paris, pp. 276–284 (2007)

    Google Scholar 

  15. Mairy, J.-B., Deville, Y., Van Hentenryck, P.: Reinforced adaptive large neighborhood search. In: 8th Workshop on Local Search techniques in Constraint Satisfaction (LSCS 2011). A Satellite Workshop of CP, Perugia, Italy (2011)

    Google Scholar 

  16. Mairy, J.-B., Schaus, P., Deville, Y.: Generic adaptive heuristics for large neighborhood search. In: Seventh International Workshop on Local Search Techniques in Constraint Satisfaction (LSCS 2010). A Satellite Workshop of CP (2010)

    Google Scholar 

  17. Timothy Marler, R., Arora, J.S.: Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization 26(6), 369–395 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  18. Mehta, D., O’Sullivan, B., Simonis, H.: Comparing solution methods for the machine reassignment problem. In: Milano, M. (ed.) CP 2012. LNCS, vol. 7514, pp. 782–797. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  19. Michel, L., Shvartsman, A., Sonderegger, E., Van Hentenryck, P.: Optimal deployment of eventually-serializable data services. In: Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 188–202 (2008)

    Google Scholar 

  20. OscaR Team. OscaR: Scala in OR (2012), https://bitbucket.org/oscarlib/oscar .

  21. Pacino, D., Van Hentenryck, P.: Large neighborhood search and adaptive randomized decompositions for flexible jobshop scheduling. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, vol. 3, pp. 1997–2002. AAAI Press (2011)

    Google Scholar 

  22. Perron, L., Shaw, P., Furnon, V.: Propagation guided large neighborhood search. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 468–481. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  23. Schaus, P., Van Hentenryck, P., Monette, J.N., Coffrin, C., Michel, L., Deville, Y.: Solving steel mill slab problems with constraint-based techniques: Cp, lns, and cbls. Constraints 16(2), 125–147 (2011)

    Article  MathSciNet  Google Scholar 

  24. Schaus, P., Régin, J.-C., Van Schaeren, R., Dullaert, W., Raa, B.: Cardinality reasoning for bin-packing constraint: Application to a tank allocation problem. In: Milano, M. (ed.) CP 2012. LNCS, vol. 7514, pp. 815–822. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  25. Shaw, P.: Using constraint programming and local search methods to solve vehicle routing problems. In: Maher, M.J., Puget, J.-F. (eds.) CP 1998. LNCS, vol. 1520, pp. 417–431. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  26. Ulungu, E.L., Teghem, J.: Multi-objective combinatorial optimization problems: A survey. Journal of Multi-Criteria Decision Analysis 3(2), 83–104 (1994)

    Article  MATH  Google Scholar 

  27. Van Wassenhove, L.N., Gelders, L.F.: Solving a bicriterion scheduling problem. European Journal of Operations Research 4, 42–48 (1980)

    Article  Google Scholar 

  28. Gandibleux, X.: A collection of test instances for multiobjective combinatorial optimization problems (2013), http://xgandibleux.free.fr/MOCOlib/

  29. Zitzler, E., Laumanns, M., Thiele, L.: Spea2: Improving the strength pareto evolutionary algorithm (2001)

    Google Scholar 

  30. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., da Fonseca, V.G.: Performance assessment of multiobjective optimizers: An analysis and review. IEEE Transactions on Evolutionary Computation 7(2), 117–132 (2003)

    Article  Google Scholar 

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Authors and Affiliations

  1. ICTEAM, UCLouvain, Place sainte barbe 2, 1348, Louvain-la-Neuve, Belgium

    Pierre Schaus & Renaud Hartert

Authors
  1. Pierre Schaus
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  2. Renaud Hartert
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Editors and Affiliations

  1. School of Information and Communication Technology, KTH Royal Institute of Technology, P.O. Box Forum 120, 16440, Kista, Sweden

    Christian Schulte

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Schaus, P., Hartert, R. (2013). Multi-Objective Large Neighborhood Search. In: Schulte, C. (eds) Principles and Practice of Constraint Programming. CP 2013. Lecture Notes in Computer Science, vol 8124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40627-0_46

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  • DOI: https://doi.org/10.1007/978-3-642-40627-0_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40626-3

  • Online ISBN: 978-3-642-40627-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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