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International Conference on Hybrid Intelligent Systems

HIS 2020: Hybrid Intelligent Systems pp 627–637Cite as

Parameter Selection for Ant Colony Optimization for Solving the Travelling Salesman Problem Based on the Problem Size

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1375))

Abstract

This paper describes the parameter setting for the Ant Colony Optimization (ACO) algorithm to find optimal solutions for the Travelling Salesman Problem (TSP). The TSP is a classical combinatorial optimization problem classified as NP-hard. The ACO algorithm, with the correct parameter setting, is known for good performance on NP-hard problems. According to the number of cities in the TSP the parameters of the ACO algorithms must be changed and the size of the ant colony adjusted. This paper analyzes parameter tuning in ACO and suggests specific parameter settings. The results are compared with already published recommendations. It is shown that the existing results can be significantly improved.

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References

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Author information

Authors and Affiliations

  1. Institute for Information Systems, School of Business, University of Applied Sciences and Arts Northwestern Switzerland, Olten, Basel, Switzerland

    Philipp Kempter, Martin Peter Schmitz, Thomas Hanne & Rolf Dornberger

Authors
  1. Philipp Kempter
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  2. Martin Peter Schmitz
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  3. Thomas Hanne
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  4. Rolf Dornberger
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Corresponding author

Correspondence to Thomas Hanne .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Institute for Information Systems, University of Applied Sciences and Arts Northwestern Switzerland, Olten, Solothurn, Switzerland

    Thomas Hanne

  3. Division of Graduate Studies, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, AL, USA

    Niketa Gandhi

  5. Universidade Federal da Bahia, Salvador, Bahia, Brazil

    Tatiane Nogueira Rios

  6. Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

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Kempter, P., Schmitz, M.P., Hanne, T., Dornberger, R. (2021). Parameter Selection for Ant Colony Optimization for Solving the Travelling Salesman Problem Based on the Problem Size. In: Abraham, A., Hanne, T., Castillo, O., Gandhi, N., Nogueira Rios, T., Hong, TP. (eds) Hybrid Intelligent Systems. HIS 2020. Advances in Intelligent Systems and Computing, vol 1375. Springer, Cham. https://doi.org/10.1007/978-3-030-73050-5_61

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