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Genetic Programming and Evolvable Machines

, Volume 3, Issue 4, pp 345–361 | Cite as

Fast Ant Colony Optimization on Runtime Reconfigurable Processor Arrays

  • Daniel Merkle
  • Martin Middendorf
Article

Abstract

Ant Colony Optimization (ACO) is a metaheuristic used to solve combinatorial optimization problems. As with other metaheuristics, like evolutionary methods, ACO algorithms often show good optimization behavior but are slow when compared to classical heuristics. Hence, there is a need to find fast implementations for ACO algorithms. In order to allow a fast parallel implementation, we propose several changes to a standard form of ACO algorithms. The main new features are the non-generational approach and the use of a threshold based decision function for the ants. We show that the new algorithm has a good optimization behavior and also allows a fast implementation on reconfigurable processor arrays. This is the first implementation of the ACO approach on a reconfigurable architecture. The running time of the algorithm is quasi-linear in the problem size n and the number of ants on a reconfigurable mesh with n2 processors, each provided with only a constant number of memory words.

ACO reconfigurable architectures quadratic assignment 

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Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Daniel Merkle
    • 1
  • Martin Middendorf
    • 1
  1. 1.Parallel Computing and Complex Systems Group, Faculty of Mathematics and Computer ScienceUniversity of LeipzigLeipzigGermany

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