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International Conference on Computational Collective Intelligence

ICCCI 2017: Computational Collective Intelligence pp 87–96Cite as

Improving ACO Convergence with Parallel Tempering

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10449))

Abstract

Parallel Tempering (PT) is an efficient Monte Carlo simulation method known from statistical physics. We present a novel PT-based Ant Colony Optimization algorithm (PTACO) in which multiple replicas of the Ant Colony System enhanced with a temperature parameter (ACST) are executed in parallel. Based on computational experiments on a set of TSP and ATSP instances we show that the PTACO converges (in terms of solutions quality) significantly faster than the ACS and is competitive to the state-of-the-art Ant Colony Extended algorithm.

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Notes

  1. 1.

    Source code is available at https://github.com/RSkinderowicz/PTACO.

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Acknowledgments

This research was supported in part by PL-Grid Infrastructure.

Author information

Authors and Affiliations

  1. Institute of Computer Science, Silesia University, Sosnowiec, Poland

    Rafał Skinderowicz

Authors
  1. Rafał Skinderowicz
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Correspondence to Rafał Skinderowicz .

Editor information

Editors and Affiliations

  1. Department of Information Systems, Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Department of Computer Science, University of Cyprus, Nicosia, Cyprus

    George A. Papadopoulos

  3. Department of Information Systems, Gdynia Maritime University, Gdynia, Poland

    Piotr Jędrzejowicz

  4. Department of Information Systems, Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  5. Department of Information Systems, University of Münster, Münster, Germany

    Gottfried Vossen

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Skinderowicz, R. (2017). Improving ACO Convergence with Parallel Tempering. In: Nguyen, N., Papadopoulos, G., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2017. Lecture Notes in Computer Science(), vol 10449. Springer, Cham. https://doi.org/10.1007/978-3-319-67077-5_9

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  • DOI: https://doi.org/10.1007/978-3-319-67077-5_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67076-8

  • Online ISBN: 978-3-319-67077-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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