S-ACO: An Ant-Based Approach to Combinatorial Optimization Under Uncertainty

  • Walter J. Gutjahr
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3172)


A general-purpose, simulation-based algorithm S-ACO for solving stochastic combinatorial optimization problems by means of the ant colony optimization (ACO) paradigm is investigated. Whereas in a prior publication, theoretical convergence of S-ACO to the globally optimal solution has been demonstrated, the present article is concerned with an experimental study of S-ACO on two stochastic problems of fixed-routes type: First, a pre-test is carried out on the probabilistic traveling salesman problem. Then, more comprehensive tests are performed for a traveling salesman problem with time windows (TSPTW) in the case of stochastic service times. As a yardstick, a stochastic simulated annealing (SSA) algorithm has been implemented for comparison. Both approaches are tested at randomly generated problem instances of different size. It turns out that S-ACO outperforms the SSA approach on the considered test instances. Some conclusions for fine-tuning S-ACO are drawn.


Test Instance Vehicle Route Problem Construction Graph Service Engineer Random Scenario 
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© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Walter J. Gutjahr
    • 1
  1. 1.Dept. of Statistics and Decision Support SystemsUniversity of Vienna 

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