GSOA: Growing Self-Organizing Array - Unsupervised learning for the Close-Enough Traveling Salesman Problem and other routing problems

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00322520" target="_blank" >RIV/68407700:21230/18:00322520 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0925231218306647" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0925231218306647</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.neucom.2018.05.079" target="_blank" >10.1016/j.neucom.2018.05.079</a>

Result language

angličtina

Original language name

GSOA: Growing Self-Organizing Array - Unsupervised learning for the Close-Enough Traveling Salesman Problem and other routing problems

Original language description

This paper presents a novel unsupervised learning procedure called the Growing Self-Organizing Array (GSOA) that is inspired by principles of the self-organizing maps for the Traveling Salesman Problem (TSP). The proposed procedure is a consolidation of principles deployed in solution of several variants of the generalized TSP with Neighborhoods (TSPN) for which the main ideas of the proposed unsupervised learning already demonstrates a wide range of applicability. The herein presented learning procedure is a conceptually simple algorithm which outperforms previous self-organizing map based approaches for the TSP in terms of the solution quality and required computational time. The main benefit of the proposed learning procedure is in solving routing problems that combine a combinatorial solution of some variant of the TSP with the continuous optimization, i.e., problems where it is needed to determine a sequence of visits to the given sets with determination of the particular waypoint location from each (possibly infinite) set. Low computational requirements of the proposed method are demonstrated in a solution of the Close-Enough Traveling Salesman Problem (CETSP), which is a special case of the TSPN with the disk-shaped neighborhoods. The results indicate the proposed procedure provides competitive solutions to the existing heuristics while it is about one order of magnitude faster and at least about two orders of magnitude faster than a heuristic solution of the discretized variant of the CETSP considered as the Generalized TSP. (C) 2018 Elsevier B.V. All rights reserved.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GJ15-09600Y" target="_blank" >GJ15-09600Y: Adaptive Informative Path Planning in Autonomous Data Collection in Dynamic Unstructured Environments</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Neurocomputing

ISSN

0925-2312

e-ISSN

1872-8286

Volume of the periodical

312

Issue of the periodical within the volume

October

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

15

Pages from-to

120-134

UT code for WoS article

000438668100011

EID of the result in the Scopus database

2-s2.0-85048745182