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An Improvement Heuristic Based on Variable Neighborhood Search for a Dynamic Orienteering Problem

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Evolutionary Computation in Combinatorial Optimization (EvoCOP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12692))

Abstract

The Dynamic Orienteering Problem (DOP) is studied where nodes change their value over time. An improvement heuristic that is based on Variable Neighborhood Search is proposed for the DOP. The new heuristic is experimentally compared with two heuristics that are based on state-of-the-art algorithms for the static Orienteering Problem. For the experiments several benchmark instances are used as well as instances that are generated from existing road networks. The results show that the new heuristic outperforms the other heuristics with respect to several evaluation criteria and different measures for run time. An additional experiment shows that the new heuristic can be easily adapted to become a standalone algorithm that does not need given initial solutions. The standalone version obtains better results than two state-of-the-art algorithms.

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Acknowledgements

This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - project number 392050753.

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Authors and Affiliations

  1. Swarm Intelligence and Complex Systems Group, Institute of Computer Science, Leipzig University, Leipzig, Germany

    Hoang Thanh Le & Martin Middendorf

  2. Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

Authors
  1. Hoang Thanh Le
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  2. Martin Middendorf
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  3. Yuhui Shi
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Corresponding author

Correspondence to Hoang Thanh Le .

Editor information

Editors and Affiliations

  1. Aberystwyth University, Aberystwyth, UK

    Christine Zarges

  2. Université du Littoral Côte d'Opale, Calais, France

    Sébastien Verel

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Le, H.T., Middendorf, M., Shi, Y. (2021). An Improvement Heuristic Based on Variable Neighborhood Search for a Dynamic Orienteering Problem. In: Zarges, C., Verel, S. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2021. Lecture Notes in Computer Science(), vol 12692. Springer, Cham. https://doi.org/10.1007/978-3-030-72904-2_5

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  • DOI: https://doi.org/10.1007/978-3-030-72904-2_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72903-5

  • Online ISBN: 978-3-030-72904-2

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