Algorithm Comparison by Automatically Configurable Stochastic Local Search Frameworks: A Case Study Using Flow-Shop Scheduling Problems

  • Franco Mascia
  • Manuel López-Ibáñez
  • Jérémie Dubois-Lacoste
  • Marie-Éléonore Marmion
  • Thomas Stützle
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8457)


The benefits of hybrid stochastic local search (SLS) methods, in comparison with more classical (non-hybrid) ones are often difficult to quantify, since one has to take into account not only the final results obtained but also the effort spent on finding the best configuration of the hybrid and of the classical SLS method. In this paper, we study this trade-off by means of tools for automatic algorithm design, and, in particular, we study the generation of hybrid SLS algorithms versus selecting one classical SLS method among several. In addition, we tune the parameters of the classical SLS method separately and compare the results with the ones obtained when selection and tuning are done at the same time. We carry out experiments on two variants of the permutation flowshop scheduling problem that consider the minimization of weighted sum of completion times (PFSP-WCT) and the minimization of weighted tardiness (PFSP-WCT). Our results indicate that the hybrid algorithms we instantiate are able to match and improve over the best classical SLS method.


Local Search Greedy Randomize Adaptive Search Procedure Local Search Procedure Iterate Local Search Total Weighted Tardiness 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Franco Mascia
    • 1
  • Manuel López-Ibáñez
    • 1
  • Jérémie Dubois-Lacoste
    • 1
  • Marie-Éléonore Marmion
    • 2
  • Thomas Stützle
    • 1
  1. 1.IRIDIA, CoDEUniversité Libre de BruxellesBrusselsBelgium
  2. 2.LIFLUniversité Lille 1, Inria LilleFrance

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