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Hyper-Reactive Tabu Search for MaxSAT

  • Carlos Ansótegui
  • Britta Heymann
  • Josep Pon
  • Meinolf Sellmann
  • Kevin TierneyEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11353)

Abstract

Local search metaheuristics have been developed as a general tool for solving hard combinatorial search problems. However, in practice, metaheuristics very rarely work straight out of the box. An expert is frequently needed to experiment with an approach and tweak parameters, remodel the problem, and adjust search concepts to achieve a reasonably effective approach. Reactive search techniques aim to liberate the user from having to manually tweak all of the parameters of their approach. In this paper, we focus on one of the most well-known and widely used reactive techniques, reactive tabu search (RTS) [7], and propose a hyper-parameterized tabu search approach that dynamically adjusts key parameters of the search using a learned strategy. Experiments on MaxSAT show that this approach can lead to state-of-the-art performance without any expert user involvement, even when the metaheuristic knows nothing more about the underlying combinatorial problem than how to evaluate the objective function.

Notes

Acknowledgement

The authors would like to thank the Paderborn Center for Parallel Computation (PC\(^2\)) for the use of the OCuLUS cluster. This work was financially supported in part by TIN2016-76573-C2-2-P.

References

  1. 1.
    Ansótegui, C., Bacchus, F., Järvisalo, M., Martins, R.: MaxSAT Evaluation (2017). http://mse17.cs.helsinki.fi
  2. 2.
    Ansotegui, C., Malitsky, Y., Samulowitz, H., Sellmann, M., Tierney, K.: Model-based geneticalgorithms for algorithm configuration. In: IJCAI, pp. 733–739 (2015)Google Scholar
  3. 3.
    Ansotegui, C., Sellmann, M., Tierney, K.: A gender-based genetic algorithm for the automatic configuration of algorithms. In: CP, pp. 142–157 (2009)Google Scholar
  4. 4.
    Ansótegui, C., Gabas, J., Malitsky, Y., Sellmann, M.: Maxsat by improved instance-specific algorithm configuration. Artif. Intell. 235, 26–39 (2016)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Ansótegui, C., Pon, J., Sellmann, M., Tierney, K.: Reactive dialectic search portfolios for maxsat. In: AAAI Conference on Artificial Intelligence (2017)Google Scholar
  6. 6.
    Argelich, J., Li, C., Manyà, F., Planes, J.: MaxSAT Evaluation (2016). www.maxsat.udl.cat
  7. 7.
    Battiti, R., Tecchiolli, G.: The reactive tabu search. ORSA J. Comput. 6(2), 126–140 (1994)CrossRefGoogle Scholar
  8. 8.
    Battiti, R., Brunato, M., Mascia, F.: Reactive Search and Intelligent Optimization, vol. 45. Springer Science & Business Media, Berlin (2008)zbMATHGoogle Scholar
  9. 9.
    Burke, E., Kendall, G., Newall, J., Hart, E., Ross, P., Schulenburg, S.: Hyper-heuristics: an emerging direction in modern search technology. Handbook of metaheuristics, pp. 457–474 (2003)Google Scholar
  10. 10.
    Burke, E.K., Gendreau, M., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., Qu, R.: Hyper-heuristics: a survey of the state of the art. J. Oper. Res. Soc. 64(12), 1695–1724 (2013)CrossRefGoogle Scholar
  11. 11.
    Doerr, B., Doerr, C.: Optimal parameter choices through self-adjustment: Applying the 1/5-th rule in discrete settings. In: GECCO, pp. 1335–1342 (2015)Google Scholar
  12. 12.
    Doerr, B., Doerr, C.: Optimal static and self-adjusting parameter choices for the \((1+(\lambda ,\lambda ))(1+(\lambda ,\lambda ))\)genetic algorithm. Algorithmica (2017)Google Scholar
  13. 13.
    Glover, F., Laguna, M.: Tabu search. In: Handbook of Combinatorial Optimization, pp. 3261–3362. Springer, Berlin (2013)Google Scholar
  14. 14.
    Glover, F., Laguna, M., Martí, R.: Principles of tabu search. In: Gonzalez, T. (ed.) Handbook of Approximation Algorithms and Metaheuristics (2007)Google Scholar
  15. 15.
    Kadioglu, S., Malitsky, Y., Sellmann, M., Tierney, K.: ISAC-instance-specific algorithm configuration. In: Coelho, H., Studer, R., Wooldridge, M. (eds.) ECAI. FAIA, vol. 215, pp. 751–756 (2010)Google Scholar
  16. 16.
    Kadioglu, S., Sellmann, M.: Dialectic search. CP, pp. 486–500 (2009)Google Scholar
  17. 17.
    KhudaBukhsh, A., Xu, L., Hoos, H., Leyton-Brown, K.: SATenstein: automatically building local search sat solvers from components. In: IJCAI, pp. 517–524 (2009)Google Scholar
  18. 18.
    Leventhal, D., Sellmann, M.: The accuracy of search heuristics: an empirical study on knapsack problems. In: Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 142–157 (2008)Google Scholar
  19. 19.
    Leyton-Brown, K., Nudelman, E., Andrew, G., McFadden, J., Shoham, Y.: A portfolio approach to algorithm selection. In: IJCAI, pp. 1542–1543 (2003)Google Scholar
  20. 20.
    Malitsky, Y., Sabharwal, A., Samulowitz, H., Sellmann, M.: Algorithm portfolios based on cost-sensitive hierarchical clustering. In: IJCAI, pp. 608–614 (2013)Google Scholar
  21. 21.
    Mısır, M., Verbeeck, K., De Causmaecker, P., Berghe, G.V.: An intelligent hyper-heuristic framework for chesc 2011. In: Learning and Intelligent Optimization, pp. 461–466. Springer, Berlin (2012)Google Scholar
  22. 22.
    Özcan, E., Mısır, M., Ochoa, G., Burke, E.K.: A reinforcement learning: great-deluge hyper-heuristic. In: Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends: Advancements and Trends, vol. 34 (2012)Google Scholar
  23. 23.
    Safarpour, S., Mangassarian, H., Veneris, A., Liffiton, M., Sakallah, K.: Improved design debugging using maximum satisfiability. In: Formal Methods in Computer Aided Design, pp. 13–19. IEEE (2007)Google Scholar
  24. 24.
    Sugawara, T.: Maxroster: solver description. In: MaxSAT Evaluation 2017, p. 12 (2017)Google Scholar
  25. 25.
    Vasquez, M., Hao, J.: A “logic-constrained” knapsack formulation and a tabu algorithm for the daily photograph scheduling of an earth observation satellite. Comput. Optim. Appl. 20(2), 137–157 (2001)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Xu, H., Rutenbar, R., Sakallah, K.: sub-SAT: a formulation for relaxed boolean satisfiability with applications in routing. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 22(6), 814–820 (2003)CrossRefGoogle Scholar
  27. 27.
    Xu, L., Hoos, H., Leyton-Brown, K.: Hydra: automatically configuring algorithms for portfolio-based selection. In: AAAI, pp. 210–216 (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Carlos Ansótegui
    • 1
  • Britta Heymann
    • 3
  • Josep Pon
    • 1
  • Meinolf Sellmann
    • 4
  • Kevin Tierney
    • 2
    Email author
  1. 1.DIEIUniversitat de LleidaLleidaSpain
  2. 2.Bielefeld UniversityBielefeldGermany
  3. 3.ORCONOMY GmbH and Paderborn UniversityPaderbornGermany
  4. 4.General ElectricNiskayunaUSA

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