Traveling salesman should not be greedy: domination analysis of greedy-type heuristics for the TSP

By G. Gutin, Anders Yeo, A. Zverovich. 2001

288 citations recorded by the source corpus.

Abstract Computational experiments show that the greedy algorithm (GR) and the nearest neighbor algorithm (NN), popular choices for tour construction heuristics, work at acceptable level for the Euclidean TSP, but produce very poor results for the general Symmetric and Asymmetric TSP (STSP and ATSP). We prove that for every n⩾2 there is an instance of ATSP (STSP) on n vertices for which GR finds the worst tour. The same result holds for NN. We also analyze the repetitive NN (RNN) that starts NN from every vertex and chooses the best tour obtained. We prove that, for the ATSP, RNN always produces a tour, which is not worse than at least n/2−1 other tours, but for some instance it finds a tour, which is not worse than at most n−2 other tours, n⩾4. We also show that, for some instance of the STSP on n⩾4 vertices, RNN produces a tour not worse than at most 2n−3 tours. These results are in sharp contrast to earlier results by Gutin and Yeo, and Punnen and Kabadi, who proved that, for the ATSP, there are tour construction heuristics, including some popular ones, that always build a tour not worse than at least (n−2)! tours.

Subfields: Computational Theory and Mathematics, Industrial and Manufacturing Engineering, Management Science and Operations Research

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In Computational Theory and Mathematics

In Industrial and Manufacturing Engineering

Zoubin Ghahramani

In Management Science and Operations Research

Citations in the corpus

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning (2019, methodology): “Therefore, to build a logical topology after each change in the physical topology, we propose a simple heuristic method that is based on the nearest neighbor (NN) heuristic Gutin et al. (2002).”
Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions (2015, background): “Greedy choice making can be severely detrimental, for example, a greedy approach to the travelling salesman problem could potentially lead to the uniquely worst global solution [11].”
Purposeful selection of variables in logistic regression (2008, background): “Hill-climbing and greedy algorithms are mathematical optimization techniques used in artificial intelligence, which work well on certain problems, but they fail to produce optimal solutions for many others [3-6].”