The node weight dependent Traveling Salesperson Problem: Approximation algorithms and randomized search heuristics

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dc.contributor.authorBossek, J.
dc.contributor.authorCasel, K.
dc.contributor.authorKerschke, P.
dc.contributor.authorNeumann, F.
dc.contributor.conferenceGenetic and Evolutionary Computation Conference (GECCO) (8 Jul 2020 - 12 Jul 2020 : Cancún, Mexico)
dc.date.issued2020
dc.description.abstractSeveral 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.
dc.description.statementofresponsibilityJakob Bossek, Katrin Casel, Pascal Kerschke, Frank Neumann
dc.identifier.citationProceedings of the 2020 Genetic and Evolutionary Computation Conference (GECCO'20), 2020, vol.abs/2002.01070, pp.1286-1294
dc.identifier.doi10.1145/3377930.3390243
dc.identifier.isbn9781450371285
dc.identifier.orcidBossek, J. [0000-0002-4121-4668]
dc.identifier.orcidNeumann, F. [0000-0002-2721-3618]
dc.identifier.urihttp://hdl.handle.net/2440/126984
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.publisher.placeNew York
dc.rights© 2020 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery.
dc.source.urihttps://dl.acm.org/doi/proceedings/10.1145/3377930
dc.subjectEvolutionary algorithms; dynamic optimization; running time analysis; theory
dc.titleThe node weight dependent Traveling Salesperson Problem: Approximation algorithms and randomized search heuristics
dc.typeConference paper
pubs.publication-statusPublished

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