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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/GJ15-09600Y" target="_blank" >GJ15-09600Y: Adaptivní plánování v úlohách autonomního sběru dat v nestrukturovaném prostředí</a><br>

Návaznosti

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

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

Neurocomputing

ISSN

0925-2312

e-ISSN

1872-8286

Svazek periodika

312

Číslo periodika v rámci svazku

October

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

15

Strana od-do

120-134

Kód UT WoS článku

000438668100011

EID výsledku v databázi Scopus

2-s2.0-85048745182