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<p>This paper investigates the systematic and complete usage of <i>k</i>-opt permutations with<br><i>k</i> = 2 . . . 6 in application to local optimization of symmetric two-dimensional instances up to<br>10<sup>7</sup> points. The proposed method utilizes several techniques for accelerating the processing, such that<br>good tours can be achieved in limited time: candidates selection based on Delaunay triangulation,<br>precomputation of a sparse distance matrix, two-level data structure, and parallel processing based<br>on multithreading. The proposed approach finds good tours (excess of 0.72–8.68% over best-known<br>tour) in a single run within 30 min for instances with more than 10<sup>5</sup> points and specifically 3.37% for<br>the largest examined tour containing 10<sup>7</sup> points. The new method proves to be competitive with a<br>state-of-the-art approach based on the Lin–Kernigham–Helsgaun method (LKH) when applied to<br>clustered instances.<br><br></p>
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Strutz, Tilo (2023): Re-Designing the Wheel for Systematic Travelling Salesmen. Algorithms 2023, 91 (2). DOI: 10.3390/a16020091
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https://www.hs-coburg.de/publikation/3439-re-designing-the-wheel-for-systematic-travelling-salesmen/
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Re-Designing the Wheel for Systematic Travelling Salesmen
This paper investigates the systematic and complete usage of k-opt permutations with
k = 2 . . . 6 in application to local optimization of symmetric two-dimensional instances up to
107 points. The proposed method utilizes several techniques for accelerating the processing, such that
good tours can be achieved in limited time: candidates selection based on Delaunay triangulation,
precomputation of a sparse distance matrix, two-level data structure, and parallel processing based
on multithreading. The proposed approach finds good tours (excess of 0.72–8.68% over best-known
tour) in a single run within 30 min for instances with more than 105 points and specifically 3.37% for
the largest examined tour containing 107 points. The new method proves to be competitive with a
state-of-the-art approach based on the Lin–Kernigham–Helsgaun method (LKH) when applied to
clustered instances.
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