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Modelling ACO: Composed Permutation Problems

  • Daniel Merkle
  • Martin Middendorf
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2463)

Abstract

The behaviour of Ant Colony Optimization (ACO) algorithms is studied on optimization problems that are composed of different types of subproblems. Numerically exact results are derived using a deterministic model for ACO that is based on the average expected behaviour of the artificial ants. These computations are supplemented by test runs with an ACO algorithm on the same problem instances. It is shown that different scaling of the objective function on isomorphic subproblems has a strong influence on the optimization behaviour of ACO. Moreover, it is shown that ACOs behaviour on a subproblem depends heavily on the type of the other subproblems. This is true even when the subproblems are independent in the sense that the value of the objective function is the sum of the qualities of the solutions of the subproblems. We propose two methods for handling scaling problems (pheromone update masking and rescaling of the objective function) that can improve ACOs behaviour. Consequences of our findings for using ACO on real-world problems are pointed out.

Keywords

Problem Instance Solution Quality Cost Matrix Pure Competition Pheromone Matrice 
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 2002

Authors and Affiliations

  • Daniel Merkle
    • 1
  • Martin Middendorf
    • 2
  1. 1.Institute for Applied Computer Science and Formal Description MethodsUniversity of KarlsruheGermany
  2. 2.Computer Science GroupCatholic University of Eichstätt-IngolstadtGermany

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