Automatically Configuring Multi-objective Local Search Using Multi-objective Optimisation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10173)


Automatic algorithm configuration (AAC) is becoming an increasingly crucial component in the design of high-performance solvers for many challenging combinatorial optimisation problems. This raises the question how to most effectively leverage AAC in the context of building or optimising multi-objective optimisation algorithms, and specifically, multi-objective local search procedures. Because the performance of multi-objective optimisation algorithms cannot be fully characterised by a single performance indicator, we believe that AAC for multi-objective local search should make use of multi-objective configuration procedures. We test this belief by using MO-ParamILS to automatically configure a highly parametric iterated local search framework for the classical and widely studied bi-objective permutation flowshop problem. To the best of our knowledge, this is the first time a multi-objective optimisation algorithm is automatically configured in a multi-objective fashion, and our results demonstrate that this approach can produce very good results as well as interesting insights into the efficacy of various strategies and components of a flexible multi-objective local search framework.


Algorithm configuration Multi-objective optimisation Local search Permutation flowshop scheduling 


  1. 1.
    Bezerra, L.C.T.: A component-wise approach to multi-objective evolutionary algorithms. Ph.D. thesis, IRIDIA, Université Libre de Bruxelles, Belgium, July 2016Google Scholar
  2. 2.
    Blot, A., Hoos, H.H., Jourdan, L., Kessaci-Marmion, M.É., Trautmann, H.: MO-ParamILS: a multi-objective automatic algorithm configuration framework. In: Festa, P., Sellmann, M., Vanschoren, J. (eds.) LION 2016. LNCS, vol. 10079, pp. 32–47. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-50349-3_3 CrossRefGoogle Scholar
  3. 3.
    Cahon, S., Melab, N., Talbi, E.: Paradiseo: a framework for the reusable design of parallel and distributed metaheuristics. JoH 10(3), 357–380 (2004)zbMATHGoogle Scholar
  4. 4.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE TEVC 6(2), 182–197 (2002)Google Scholar
  5. 5.
    Drugan, M.M., Thierens, D.: Path-guided mutation for stochastic pareto local search algorithms. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN 2010. LNCS, vol. 6238, pp. 485–495. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-15844-5_49 Google Scholar
  6. 6.
    Drugan, M.M., Thierens, D.: Stochastic Pareto local search: Pareto neighbourhood exploration and perturbation strategies. JoH 18(5), 727–766 (2012)Google Scholar
  7. 7.
    Dubois-Lacoste, J., López-Ibáñez, M., Stützle, T.: Anytime Pareto local search. EJOR 243(2), 369–385 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Dubois-Lacoste, J., López-Ibáñez, M., Stützle, T.: A hybrid TP+PLS algorithm for bi-objective flow-shop scheduling problems. C&OR 38(8), 1219–1236 (2011)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Hoos, H.H., Stützle, T.: Stochastic Local Search: Foundations and Applications. Elsevier/Morgan Kaufmann, San Francisco (2004)zbMATHGoogle Scholar
  10. 10.
    Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration. In: Coello, C.A.C. (ed.) LION 2011. LNCS, vol. 6683, pp. 507–523. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-25566-3_40 CrossRefGoogle Scholar
  11. 11.
    Hutter, F., Hoos, H.H., Leyton-Brown, K., Stützle, T.: ParamILS: an automatic algorithm configuration framework. JAIR 36, 267–306 (2009)zbMATHGoogle Scholar
  12. 12.
    Knowles, J., Corne, D.: On metrics for comparing nondominated sets. In: IEEE CEC, vol. 1, pp. 711–716 (2002)Google Scholar
  13. 13.
    Liefooghe, A., Humeau, J., Mesmoudi, S., Jourdan, L., Talbi, E.: On dominance-based multiobjective local search: design, implementation and experimental analysis on scheduling and traveling salesman problems. JoH 18(2), 317–352 (2012)Google Scholar
  14. 14.
    López-Ibáñez, M., Dubois-Lacoste, J., Stützle, T., Birattari, M.: The irace package, iterated race for automatic algorithm configuration. Technical report TR/IRIDIA/2011-004, IRIDIA, Université Libre de Bruxelles, Belgium (2011)Google Scholar
  15. 15.
    Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated local search. In: Glover, F., Kochenberger, G.A. (eds.) Handbook of Metaheuristics, pp. 320–353. Springer, New York (2003)CrossRefGoogle Scholar
  16. 16.
    Minella, G., Ruiz, R., Ciavotta, M.: A review and evaluation of multiobjective algorithms for the flowshop scheduling problem. IJOC 20(3), 451–471 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Okabe, T., Jin, Y., Sendhoff, B.: A critical survey of performance indices for multi-objective optimisation. In: IEEE CEC, vol. 2, pp. 878–885 (2003)Google Scholar
  18. 18.
    Paquete, L., Chiarandini, M., Stützle, T.: Pareto local optimum sets in the biobjective traveling salesman problem: an experimental study. In: Gandibleux, X., Sevaux, M., Sörensen, K., T’kindt, V. (eds.) Metaheuristics for Multiobjective Optimisation, pp. 177–199. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  19. 19.
    Taillard, E.: Benchmarks for basic scheduling problems. EJOC 64(2), 278–285 (1993)zbMATHGoogle Scholar
  20. 20.
    Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE TEVC 3(4), 257–271 (1999)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Université de Lille, Inria, CNRS, UMR 9189 – CRIStALLilleFrance
  2. 2.University of British ColumbiaVancouverCanada
  3. 3.Universiteit LeidenLeidenThe Netherlands

Personalised recommendations