Experimental Analysis of Pheromone-Based Heuristic Column Generation Using irace

  • Florence Massen
  • Manuel López-Ibáñez
  • Thomas Stützle
  • Yves Deville
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7919)


Pheromone-based heuristic column generation (ACO-HCG) is a hybrid algorithm that combines ant colony optimization and a MIP solver to tackle vehicle routing problems (VRP) with black-box feasibility. Traditionally, the experimental analysis of such a complex algorithm has been carried out manually by trial and error. Moreover, a full-factorial statistical analysis is infeasible due to the large number of parameters and the time required for each algorithm run. In this paper, we first automatically configure the algorithm parameters by using an automatic algorithm configuration tool. Then, we perform a basic sensitivity analysis of the tuned configuration in order to understand the significance of each parameter setting. In this way, we avoid wasting effort analyzing parameter settings that do not lead to a high-performing algorithm. Finally, we show that the tuned parameter settings improve the performance of ACO-HCG on the multi-pile VRP and the three-dimensional loading capacitated VRP.


Vehicle Rout Problem Training Instance Benchmark Instance Iterate Local Search Capacitate Vehicle Rout Problem 
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-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Florence Massen
    • 1
  • Manuel López-Ibáñez
    • 2
  • Thomas Stützle
    • 2
  • Yves Deville
    • 1
  1. 1.ICTEAMUniversité catholique de LouvainBelgium
  2. 2.IRIDIAUniversité libre de BruxellesBelgium

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