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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)

Abstract

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.

Keywords

Test Instance Vehicle Route Problem Construction Graph Service Engineer Random Scenario 
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 2004

Authors and Affiliations

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

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