A population based metaheuristic for the minimum latency problem

Boldizsár Tüű-Szabó, Péter Földesi, L. Kóczy

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In this paper we present a population based metaheuristic for solving the Minimum Latency Problem, which is the combination of bacterial evolutionary algorithm with local search techniques. The algorithm was tested on TSPLIB benchmark instances, and the results are competitive in terms of accuracy and runtimes with the state-of-the art methods. Except for two instances our algorithm found the best-known solution, and for the biggest tested instance it outperformed the best-known solution. The runtime was on average 30% faster than the most efficient method in the literature.

LanguageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Verlag
Pages113-121
Number of pages9
DOIs
Publication statusPublished - Jan 1 2019

Publication series

NameStudies in Computational Intelligence
Volume796
ISSN (Print)1860-949X

Fingerprint

Evolutionary algorithms

Keywords

  • Delivery man problem
  • Discrete optimization
  • Metaheuristic
  • Minimum latency problem
  • Traveling repairman problem

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Tüű-Szabó, B., Földesi, P., & Kóczy, L. (2019). A population based metaheuristic for the minimum latency problem. In Studies in Computational Intelligence (pp. 113-121). (Studies in Computational Intelligence; Vol. 796). Springer Verlag. https://doi.org/10.1007/978-3-030-00485-9_13

A population based metaheuristic for the minimum latency problem. / Tüű-Szabó, Boldizsár; Földesi, Péter; Kóczy, L.

Studies in Computational Intelligence. Springer Verlag, 2019. p. 113-121 (Studies in Computational Intelligence; Vol. 796).

Research output: Chapter in Book/Report/Conference proceedingChapter

Tüű-Szabó, B, Földesi, P & Kóczy, L 2019, A population based metaheuristic for the minimum latency problem. in Studies in Computational Intelligence. Studies in Computational Intelligence, vol. 796, Springer Verlag, pp. 113-121. https://doi.org/10.1007/978-3-030-00485-9_13
Tüű-Szabó B, Földesi P, Kóczy L. A population based metaheuristic for the minimum latency problem. In Studies in Computational Intelligence. Springer Verlag. 2019. p. 113-121. (Studies in Computational Intelligence). https://doi.org/10.1007/978-3-030-00485-9_13
Tüű-Szabó, Boldizsár ; Földesi, Péter ; Kóczy, L. / A population based metaheuristic for the minimum latency problem. Studies in Computational Intelligence. Springer Verlag, 2019. pp. 113-121 (Studies in Computational Intelligence).
@inbook{059eaeb3f1294e419120e924c4eecc1b,
title = "A population based metaheuristic for the minimum latency problem",
abstract = "In this paper we present a population based metaheuristic for solving the Minimum Latency Problem, which is the combination of bacterial evolutionary algorithm with local search techniques. The algorithm was tested on TSPLIB benchmark instances, and the results are competitive in terms of accuracy and runtimes with the state-of-the art methods. Except for two instances our algorithm found the best-known solution, and for the biggest tested instance it outperformed the best-known solution. The runtime was on average 30{\%} faster than the most efficient method in the literature.",
keywords = "Delivery man problem, Discrete optimization, Metaheuristic, Minimum latency problem, Traveling repairman problem",
author = "Boldizs{\'a}r T{\"u}ű-Szab{\'o} and P{\'e}ter F{\"o}ldesi and L. K{\'o}czy",
year = "2019",
month = "1",
day = "1",
doi = "10.1007/978-3-030-00485-9_13",
language = "English",
series = "Studies in Computational Intelligence",
publisher = "Springer Verlag",
pages = "113--121",
booktitle = "Studies in Computational Intelligence",

}

TY - CHAP

T1 - A population based metaheuristic for the minimum latency problem

AU - Tüű-Szabó, Boldizsár

AU - Földesi, Péter

AU - Kóczy, L.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - In this paper we present a population based metaheuristic for solving the Minimum Latency Problem, which is the combination of bacterial evolutionary algorithm with local search techniques. The algorithm was tested on TSPLIB benchmark instances, and the results are competitive in terms of accuracy and runtimes with the state-of-the art methods. Except for two instances our algorithm found the best-known solution, and for the biggest tested instance it outperformed the best-known solution. The runtime was on average 30% faster than the most efficient method in the literature.

AB - In this paper we present a population based metaheuristic for solving the Minimum Latency Problem, which is the combination of bacterial evolutionary algorithm with local search techniques. The algorithm was tested on TSPLIB benchmark instances, and the results are competitive in terms of accuracy and runtimes with the state-of-the art methods. Except for two instances our algorithm found the best-known solution, and for the biggest tested instance it outperformed the best-known solution. The runtime was on average 30% faster than the most efficient method in the literature.

KW - Delivery man problem

KW - Discrete optimization

KW - Metaheuristic

KW - Minimum latency problem

KW - Traveling repairman problem

UR - http://www.scopus.com/inward/record.url?scp=85054763160&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85054763160&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-00485-9_13

DO - 10.1007/978-3-030-00485-9_13

M3 - Chapter

T3 - Studies in Computational Intelligence

SP - 113

EP - 121

BT - Studies in Computational Intelligence

PB - Springer Verlag

ER -