k-RNN: Extending NN-heuristics for the TSP
Artificial Intelligence
2018-10-19 v1
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.
Cite
@article{arxiv.1810.08059,
title = {k-RNN: Extending NN-heuristics for the TSP},
author = {Nikolas Klug and Alok Chauhan and Ramesh Ragala and V Vijayakumar},
journal= {arXiv preprint arXiv:1810.08059},
year = {2018}
}