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Estimation-based ant colony optimization and local search for the probabilistic traveling salesman problem

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Abstract

The use of ant colony optimization for solving stochastic optimization problems has received a significant amount of attention in recent years. In this paper, we present a study of enhanced ant colony optimization algorithms for tackling a stochastic optimization problem, the probabilistic traveling salesman problem. In particular, we propose an empirical estimation approach to evaluate the cost of the solutions constructed by the ants. Moreover, we use a recent estimation-based iterative improvement algorithm as a local search. Experimental results on a large number of problem instances show that the proposed ant colony optimization algorithms outperform the current best algorithm tailored to solve the given problem, which also happened to be an ant colony optimization algorithm. As a consequence, we have obtained a new state-of-the-art ant colony optimization algorithm for the probabilistic traveling salesman problem.

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Authors and Affiliations

  1. IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium

    Prasanna Balaprakash, Mauro Birattari, Thomas Stützle, Zhi Yuan & Marco Dorigo

Authors
  1. Prasanna Balaprakash
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  2. Mauro Birattari
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  3. Thomas Stützle
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  4. Zhi Yuan
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  5. Marco Dorigo
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Correspondence to Prasanna Balaprakash.

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Balaprakash, P., Birattari, M., Stützle, T. et al. Estimation-based ant colony optimization and local search for the probabilistic traveling salesman problem. Swarm Intell 3, 223–242 (2009). https://doi.org/10.1007/s11721-009-0031-y

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  • DOI: https://doi.org/10.1007/s11721-009-0031-y

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