liu.seSök publikationer i DiVA
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Experimental analysis of heuristic solutions for the moving target traveling salesman problem applied to a moving targets monitoring system
Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fed Univ Rio Grande do Sul UFRGS, Brazil.
Fed Univ Rio Grande do Sul UFRGS, Brazil.
2019 (Engelska)Ingår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 136, s. 392-409Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

The Traveling Salesman Problem (TSP) is an important problem in computer science which consists in finding a path linking a set of cities so that each of then can be visited once, before the traveler comes back to the starting point. This is highly relevant because several real world problems can be mapped to it. A special case of TSP is the one in which the cities (the points to be visited) are not static as the cities, but mobile, changing their positions as the time passes. This variation is known as Moving Target TSP (MT-TSP). Emerging systems for crowd monitoring and control based on unmanned aerial vehicles (UAVs) can be mapped to this variation of the TSP problem, as a number of persons (targets) in the crowd can be assigned to be monitored by a given number of UAVs, which by their turn divide the targets among them. These target persons have to be visited from time to time, in a similar way to the cities in the traditional TSP. Aiming at finding a suitable solution for this type of crowd monitoring application, and considering the fact that exact solutions are too complex to perform in a reasonable time, this work explores and compares different heuristic methods for the intended solution. The performed experiments showed that the Genetic Algorithms present the best performance in finding acceptable solutions for the problem in restricted time and processing power situations, performing better compared to Ant Colony Optimization and Simulated Annealing Algorithms. (C) 2019 Published by Elsevier Ltd.

Ort, förlag, år, upplaga, sidor
PERGAMON-ELSEVIER SCIENCE LTD , 2019. Vol. 136, s. 392-409
Nyckelord [en]
Ant Colony Optimization; Genetic Algorithms; Simulated Annealing; Artificial intelligence; Moving target traveling salesman problem; Moving targets
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:liu:diva-160571DOI: 10.1016/j.eswa.2019.04.023ISI: 000484871300032OAI: oai:DiVA.org:liu-160571DiVA, id: diva2:1362742
Anmärkning

Funding Agencies|Brazilian Research Support Agency - CNPq; Swedens Innovation Agency - VinnovaVinnova

Tillgänglig från: 2019-10-21 Skapad: 2019-10-21 Senast uppdaterad: 2019-10-21

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

Förlagets fulltext

Sök vidare i DiVA

Av författaren/redaktören
Saar de Moraes, Rodrigo
Av organisationen
Programvara och systemTekniska fakulteten
I samma tidskrift
Expert systems with applications
Datavetenskap (datalogi)

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetricpoäng

doi
urn-nbn
Totalt: 24 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf