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k-RNN: Extending NN-heuristics for the TSP

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Abstract

In this paper we present an extension of existing Nearest-Neighbor heuristics to an algorithm called k-Repetitive-Nearest-Neighbor. The idea is to start with a tour of k nodes and then perform a Nearest-Neighbor search from there on. After doing this for all permutations of k nodes the result gets selected as the shortest tour found. Experimental results show that for 2-RNN the solutions quality remains relatively stable between about 10% to 40% above the optimum.

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Authors and Affiliations

  1. University of Augsburg, Augsburg, Germany

    Nikolas Klug

  2. VIT Chennai, Chennai, India

    Alok Chauhan, Vijayakumar V & Ramesh Ragala

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  1. Nikolas Klug
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  2. Alok Chauhan
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  3. Vijayakumar V
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  4. Ramesh Ragala
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Correspondence to Alok Chauhan.

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Klug, N., Chauhan, A., V, V. et al. k-RNN: Extending NN-heuristics for the TSP. Mobile Netw Appl 24, 1210–1213 (2019). https://doi.org/10.1007/s11036-019-01258-y

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  • DOI: https://doi.org/10.1007/s11036-019-01258-y

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