Beam-ACO Based on Stochastic Sampling: A Case Study on the TSP with Time Windows

  • Manuel López-Ibáñez
  • Christian Blum
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5851)


Beam-ACO algorithms are hybrid methods that combine the metaheuristic ant colony optimization with beam search. They heavily rely on accurate and computationally inexpensive bounding information for choosing between different partial solutions during the solution construction process. In this work we present the use of stochastic sampling as a useful alternative to bounding information in cases were computing accurate bounding information is too expensive. As a case study we choose the well-known travelling salesman problem with time windows. Our results clearly demonstrate that Beam-ACO, even when bounding information is replaced by stochastic sampling, may have important advantages over standard ACO algorithms.


Travel Salesman Problem Partial Solution Constraint Violation Assembly Line Balance Heuristic Information 
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 2009

Authors and Affiliations

  • Manuel López-Ibáñez
    • 1
  • Christian Blum
    • 1
  1. 1.Dept. Llenguatges i Sistemes InformàticsUniversitat Politècnica de CatalunyaBarcelonaSpain

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