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Ant Colony Optimization on a Budget of 1000

  • Leslie Pérez Cáceres
  • Manuel López-Ibáñez
  • Thomas Stützle
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8667)

Abstract

Ant Colony Optimization (ACO) was originally developed as an algorithmic technique for tackling NP-hard combinatorial optimization problems. Most of the research on ACO has focused on algorithmic variants that obtain high-quality solutions when computation time allows the evaluation of a very large number of candidate solutions, often in the order of millions. However, in situations where the evaluation of solutions is very costly in computational terms, only a relatively small number of solutions can be evaluated within a reasonable time. This situation may arise, for example, when evaluation requires simulation. In such a situation, the current knowledge on the best ACO strategies and the range of the best settings for various ACO parameters may not be applicable anymore. In this paper, we start an investigation of how different ACO algorithms behave if they have available only a very limited number of solution evaluations, say, 1000. We show that, after tuning the parameter settings for this type of scenario, still the original Ant System performs relatively poor compared to other ACO strategies. However, the best parameter settings for such a small evaluation budget are very different from the standard recommendations available in the literature.

Keywords

Travel Salesman Problem Solution Evaluation Quadratic Assignment Problem Heuristic Information Default Parameter Setting 
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

  • Leslie Pérez Cáceres
    • 1
  • Manuel López-Ibáñez
    • 1
  • Thomas Stützle
    • 1
  1. 1.IRIDIA, CoDEUniversité libre de BruxellesBelgium

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