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ACO-iRBA: A Hybrid Approach to TSPN with Overlapping Neighborhoods

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Simulated Evolution and Learning (SEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10593))

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Abstract

The traveling salesman problem with neighborhoods (TSPN) is a generalization of TSP and can be regarded as a combination of TSP and TPP (Touring Polygons Problem). In this paper, we propose a hybrid TSPN solution named ACO-iRBA in which the TSP and TPP tasks are tackled simultaneously by ACO (Ant Colony Optimization) and iRBA, an improved version of RBA (Rubber Band Algorithm), respectively. A major feature of ACO-iRBA is that it can properly handle situations where the neighborhoods are heavily overlapped. Experiment results on benchmark problems composed of random ellipses show that ACO-iRBA can solve TSPN instances with up to 70 regions effectively and generally produce higher quality solutions than a recent heuristic method CIH.

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Authors and Affiliations

  1. Intelligent Computing Lab, Division of Informatics, Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, People’s Republic of China

    Yuanlong Qin & Bo Yuan

Authors
  1. Yuanlong Qin
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  2. Bo Yuan
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Corresponding author

Correspondence to Bo Yuan .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  2. City University of Hong Kong, Hong Kong, Kowloon, Hong Kong

    Kay Chen Tan

  3. Victoria University of Wellington, Wellington, Wellington, New Zealand

    Mengjie Zhang

  4. Southern University of Science and Technology, Shenzhen, China

    Ke Tang

  5. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

  6. City University of Hong Kong, Kowloon Tong, Hong Kong

    Qingfu Zhang

  7. Peking University, Beijing, China

    Ying Tan

  8. University of Leipzig, Leipzig, Germany

    Martin Middendorf

  9. University of Surrey, Guildford, Surrey, United Kingdom

    Yaochu Jin

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Qin, Y., Yuan, B. (2017). ACO-iRBA: A Hybrid Approach to TSPN with Overlapping Neighborhoods. In: Shi, Y., et al. Simulated Evolution and Learning. SEAL 2017. Lecture Notes in Computer Science(), vol 10593. Springer, Cham. https://doi.org/10.1007/978-3-319-68759-9_8

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  • DOI: https://doi.org/10.1007/978-3-319-68759-9_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68758-2

  • Online ISBN: 978-3-319-68759-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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