Self-Organizing Map for the Curvature-Constrained Traveling Salesman Problem

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00307174" target="_blank" >RIV/68407700:21230/16:00307174 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-319-44781-0_59" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-319-44781-0_59</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-44781-0_59" target="_blank" >10.1007/978-3-319-44781-0_59</a>

Result language
angličtina
Original language name
Self-Organizing Map for the Curvature-Constrained Traveling Salesman Problem
Original language description
In this paper, we consider a challenging variant of the traveling salesman problem (TSP) where it is requested to determine the shortest closed curvature-constrained path to visit a set of given locations. The problem is called the Dubins traveling salesman problem in literature and its main difficulty arises from the fact that it is necessary to determine the sequence of visits to the locations together with particular headings of the vehicle at the locations. We propose to apply principles of unsupervised learning of the self-organizing map to simultaneously determine the sequence of the visits together with the headings. A feasibility of the proposed approach is supported by an extensive evaluation and comparison to existing solutions. The presented results indicate that the proposed approach provides competitive solutions to existing heuristics, especially in dense problems, where the optimal sequence of the visits cannot be determined as a solution of the Euclidean TSP.
Czech name
—
Czech description
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Project
<a href="/en/project/GA16-24206S" target="_blank" >GA16-24206S: Efficient Information Gathering with Dubins Vehicles in Persistent Monitoring and Surveillance Missions</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2016, PT II
ISBN
978-3-319-44780-3
ISSN
0302-9743
e-ISSN
—
Number of pages
9
Pages from-to
497-505
Publisher name
Springer VDI Verlag
Place of publication
Düsseldorf
Event location
Barcelona
Event date
Sep 6, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000389086400059

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