2021
DOI: 10.3389/frobt.2021.689908
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Solving the Large-Scale TSP Problem in 1 h: Santa Claus Challenge 2020

Abstract: The scalability of traveling salesperson problem (TSP) algorithms for handling large-scale problem instances has been an open problem for a long time. We arranged a so-called Santa Claus challenge and invited people to submit their algorithms to solve a TSP problem instance that is larger than 1 M nodes given only 1 h of computing time. In this article, we analyze the results and show which design choices are decisive in providing the best solution to the problem with the given constraints. There were three va… Show more

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Cited by 14 publications
(9 citation statements)
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“…The ara238025, lra498378 and lrb744710 are three instances containing hundreds of thousands of nodes, which are the very large-scale integration instances of TSP Test Data. The Santa, which has 1437195 cities, as a benchmark instance for large-scale TSPs, has been investigated thoroughly by several well-known solvers in [65]. Gaia was published by William Cook in 2019 and includes two million coordinates of stars.…”
Section: Results On Large-scale Tsp Instancesmentioning
confidence: 99%
“…The ara238025, lra498378 and lrb744710 are three instances containing hundreds of thousands of nodes, which are the very large-scale integration instances of TSP Test Data. The Santa, which has 1437195 cities, as a benchmark instance for large-scale TSPs, has been investigated thoroughly by several well-known solvers in [65]. Gaia was published by William Cook in 2019 and includes two million coordinates of stars.…”
Section: Results On Large-scale Tsp Instancesmentioning
confidence: 99%
“…The development of the neighborhood size is demonstrated in Fig. 3 for the Santa dataset [6]. As we can see, the number of neighbors remains highly limited during the initialization.…”
Section: Delaunay Shortest Edgementioning
confidence: 96%
“…They divide the problem instance into several sub-problems by clustering. However, the results for Santa Claus data consisting of 1.4M nodes were not any better than the-state-of-the-art local search algorithm utilizing efficient neighborhood search [6]. The key trick is to apply a neighborhood graph to select the breaking point for the k-opt operation in the same neighborhood.…”
Section: Introductionmentioning
confidence: 94%
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