Designing and Tuning SLS Through Animation and Graphics: An Extended Walk-Through

  • Steven Halim
  • Roland H. C. Yap
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4638)


Stochastic Local Search (SLS) is quite effective for a variety of Combinatorial (Optimization) Problems. However, the performance of SLS depends on several factors and getting it right is not trivial. In practice, SLS may have to be carefully designed and tuned to give good results. Often this is done in an ad-hoc fashion. One approach to this issue is to use a tuning algorithm for finding good parameter settings to a black-box SLS algorithm. Another approach is white-box which takes advantage of the human in the process. In this paper, we show how visualization using a generic visual tool can be effective for a white-box approach to get the right SLS behavior on the fitness landscape of the problem instances at hand. We illustrate this by means of an extended walk-through on the Quadratic Assignment Problem. At the same time, we present the human-centric tool which has been developed.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Charon, I., Hudry, O.: Mixing Different Components of Metaheuristics. In: Meta-Heuristics: Theory and Applications, pp. 589–603. Kluwer, Dordrecht (1996)Google Scholar
  2. 2.
    Birattari, M.: The Problem of Tuning Metaheuristics as seen from a machine learning perspective. PhD thesis, Université Libre de Bruxelles (2004)Google Scholar
  3. 3.
    Hoos, H., Stützle, T.: Stochastic Local Search: Foundations and Applications. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar
  4. 4.
    Adenso-Diaz, B., Laguna, M.: Fine-tuning of Algorithms Using Fractional Experimental Designs and Local Search. Operations Research 54(1), 99–114 (2006)CrossRefGoogle Scholar
  5. 5.
    Halim, S., Lau, H.: Tuning Tabu Search Strategies via Visual Diagnosis. In: Meta-Heuristics: Progress as Complex Systems Optimization, Kluwer, Dordrecht (2007)Google Scholar
  6. 6.
    Monett-Diaz, D.: +CARPS: Configuration of Metaheuristics Based on Cooperative Agents. In: International Workshop on Hybrid Metaheuristics, pp. 115–125 (2004)Google Scholar
  7. 7.
    Hutter, H., Hamadi, Y., Hoos, H., Leyton-Brown, K.: Performance Prediction and Automated Tuning of Randomized and Parametic Algorithms. In: International Conference on Principles and Practice of Constraint Programming, pp. 213–228 (2006)Google Scholar
  8. 8.
    Merz, P.: Memetic Algorithms for Combinatorial Optimization: Fitness Landscapes & Effective Search Strategies. PhD thesis, University of Siegen, Germany (2000)Google Scholar
  9. 9.
    Bartz-Beielstein, T.: Experimental Research in Evolutionary Computation: The New Experimentalism. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  10. 10.
    Klau, G., Lesh, N., Marks, J., Mitzenmacher, M.: Human-Guided Tabu Search. In: National Conference on Artificial Intelligence (AAAI), pp. 41–47 (2002)Google Scholar
  11. 11.
    Syrjakow, M., Szczerbicka, H.: Java-based Animation of Probabilistic Search Algorithms. In: International Conference on Web-based Modeling and Simulation, pp. 182–187 (1999)Google Scholar
  12. 12.
    Kadluczka, M., Nelson, P., Tirpak, T.: N-to-2-Space Mapping for Visualization of Search Algorithm Performance. In: International Conference on Tools with Artificial Intelligence, pp. 508–513 (2004)Google Scholar
  13. 13.
    Lau, H., Wan, W., Halim, S.: Tuning Tabu Search Strategies via Visual Diagnosis. In: Metaheuristics International Conference, pp. 630–636 (2005)Google Scholar
  14. 14.
    Halim, S., Yap, R., Lau, H.: Visualization for Analyzing Trajectory-Based Metaheuristic Search Algorithms. In: European Conference on Artificial Intelligence, pp. 703–704 (2006)Google Scholar
  15. 15.
    Halim, S., Yap, R., Lau, H.: Viz: A Visual Analysis Suite for Explaining Local Search Behavior. In: User Interface Software and Technology, pp. 57–66 (2006)Google Scholar
  16. 16.
    Schneider, J., Kirkpatrick, S.: Stochastic Optimization. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  17. 17.
    Taillard, E.: Robust Tabu Search for the Quadratic Assignment Problem. Parallel Computing 17, 443–455 (1991)CrossRefGoogle Scholar
  18. 18.
    Schiavinotto, T., Stützle, T.: A Review of Metrics on Permutations for Search Landscape Analysis. Computers and Operation Research 34(10), 3143–3153 (2007)zbMATHCrossRefGoogle Scholar
  19. 19.
    Graphviz: Graph Visualization Software,
  20. 20.
    Ware, C.: Information Visualization: Perception for Design. Morgan Kaufmann, San Francisco (2004)Google Scholar
  21. 21.
    QAPLIB: Quadratic assignment problem library,
  22. 22.
    Taillard, E.: Comparison of Iterative Searches for the Quadratic Assignment Problem. Location Science 3, 87–105 (1995)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Steven Halim
    • 1
  • Roland H. C. Yap
    • 1
  1. 1.School of Computing, National University of SingaporeSingapore

Personalised recommendations