Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/126984
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Type: Conference paper
Title: The node weight dependent Traveling Salesperson Problem: Approximation algorithms and randomized search heuristics
Author: Bossek, J.
Casel, K.
Kerschke, P.
Neumann, F.
Citation: Proceedings of the 2020 Genetic and Evolutionary Computation Conference (GECCO'20), 2020 / vol.abs/2002.01070, pp.1286-1294
Publisher: Association for Computing Machinery
Publisher Place: New York
Issue Date: 2020
ISBN: 9781450371285
Conference Name: Genetic and Evolutionary Computation Conference (GECCO) (08 Jul 2020 - 12 Jul 2020 : Cancún, Mexico)
Statement of
Responsibility: 
Jakob Bossek, Katrin Casel, Pascal Kerschke, Frank Neumann
Abstract: Several important optimization problems in the area of vehicle routing can be seen as variants of the classical Traveling Salesperson Problem (TSP). In the area of evolutionary computation, the Traveling Thief Problem (TTP) has gained increasing interest over the last 5 years. In this paper, we investigate the effect of weights on such problems, in the sense that the cost of traveling increases with respect to the weights of nodes already visited during a tour. This provides abstractions of important TSP variants such as the Traveling Thief Problem and time dependent TSP variants, and allows to study precisely the increase in difficulty caused by weight dependence. We provide a 3.59-approximation for this weight dependent version of TSP with metric distances and bounded positive weights. Furthermore, we conduct experimental investigations for simple randomized local search with classical mutation operators and two variants of the state-of-the-art evolutionary algorithm EAX adapted to the weighted TSP. Our results show the impact of the node weights on the position of the nodes in the resulting tour.
Keywords: Evolutionary algorithms; dynamic optimization; running time analysis; theory
Rights: © 2020 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery.
RMID: 1000014739
DOI: 10.1145/3377930.3390243
Published version: https://dl.acm.org/doi/proceedings/10.1145/3377930
Appears in Collections:Computer Science publications

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