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Parallelization strategies for Ant Colony Optimization

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Parallel Problem Solving from Nature — PPSN V (PPSN 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1498))

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Abstract

Ant Colony Optimization (ACO) is a new population oriented search metaphor that has been successfully applied to NP-hard combinatorial optimization problems. In this paper we discuss parallelization strategies for Ant Colony Optimization algorithms. We empirically test the most simple strategy, that of executing parallel independent runs of an algorithm. The empirical tests are performed applying MAX-MIN Ant System, one of the most efficient ACO algorithms, to the Traveling Salesman Problem and show that using parallel independent runs is very effective.

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Authors and Affiliations

  1. FB Informatik, FG Intellektik, TU Darmstadt, Alexanderstr. 10, D-64283, Darmstadt, Germany

    Thomas Stützle

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  1. Thomas Stützle
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Agoston E. Eiben Thomas Bäck Marc Schoenauer Hans-Paul Schwefel

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© 1998 Springer-Verlag Berlin Heidelberg

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Stützle, T. (1998). Parallelization strategies for Ant Colony Optimization. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN V. PPSN 1998. Lecture Notes in Computer Science, vol 1498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056914

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  • DOI: https://doi.org/10.1007/BFb0056914

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  • Print ISBN: 978-3-540-65078-2

  • Online ISBN: 978-3-540-49672-4

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