Ant colony optimization for satellite customer assignment

S. S. Kim, Hyong Joong Kim, V. Mani, C. H. Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper considers the meta-heuristic method of ant colony optimization to the problem of assigning customers to satellite channels. It is shown in an earlier study that finding an optimal allocation of customers to satellite channels is a difficult combinatorial optimization problem and is NP-complete. Hence, we propose an ant colony system (ACS) with strategies of ranking and Max-Min ant system (MMAS) for an effective search of the best/optimal assignment of customers to satellite channels under a dynamic environment. Our simulation results show that this methodology is successful in finding an assignment of customers to satellite channels. Three strategies, ACS with only ranking, ACS with only MMAS, and ACS with both ranking and MMAS are considered. A comparison of these strategies are presented to show the performance of each strategy.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages170-179
Number of pages10
Volume4412 LNCS
Publication statusPublished - 2007 Dec 1
Event1st International Conference on Ubiquitous Convergence Technology, ICUCT 2006 - Jeju Island, Korea, Republic of
Duration: 2006 Dec 52006 Dec 6

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4412 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other1st International Conference on Ubiquitous Convergence Technology, ICUCT 2006
CountryKorea, Republic of
CityJeju Island
Period06/12/506/12/6

Fingerprint

Ant Colony System
Ants
Ant colony optimization
Assignment
Customers
Min-max
Satellites
Ranking
Heuristic methods
Combinatorial optimization
Optimal Allocation
Heuristic Method
Dynamic Environment
Combinatorial Optimization Problem
Metaheuristics
NP-complete problem
Strategy
Methodology
Simulation

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Kim, S. S., Kim, H. J., Mani, V., & Kim, C. H. (2007). Ant colony optimization for satellite customer assignment. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4412 LNCS, pp. 170-179). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4412 LNCS).

Ant colony optimization for satellite customer assignment. / Kim, S. S.; Kim, Hyong Joong; Mani, V.; Kim, C. H.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4412 LNCS 2007. p. 170-179 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4412 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kim, SS, Kim, HJ, Mani, V & Kim, CH 2007, Ant colony optimization for satellite customer assignment. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 4412 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4412 LNCS, pp. 170-179, 1st International Conference on Ubiquitous Convergence Technology, ICUCT 2006, Jeju Island, Korea, Republic of, 06/12/5.
Kim SS, Kim HJ, Mani V, Kim CH. Ant colony optimization for satellite customer assignment. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4412 LNCS. 2007. p. 170-179. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Kim, S. S. ; Kim, Hyong Joong ; Mani, V. ; Kim, C. H. / Ant colony optimization for satellite customer assignment. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4412 LNCS 2007. pp. 170-179 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{0c12420250c147e3b346afb7dd96632e,
title = "Ant colony optimization for satellite customer assignment",
abstract = "This paper considers the meta-heuristic method of ant colony optimization to the problem of assigning customers to satellite channels. It is shown in an earlier study that finding an optimal allocation of customers to satellite channels is a difficult combinatorial optimization problem and is NP-complete. Hence, we propose an ant colony system (ACS) with strategies of ranking and Max-Min ant system (MMAS) for an effective search of the best/optimal assignment of customers to satellite channels under a dynamic environment. Our simulation results show that this methodology is successful in finding an assignment of customers to satellite channels. Three strategies, ACS with only ranking, ACS with only MMAS, and ACS with both ranking and MMAS are considered. A comparison of these strategies are presented to show the performance of each strategy.",
author = "Kim, {S. S.} and Kim, {Hyong Joong} and V. Mani and Kim, {C. H.}",
year = "2007",
month = "12",
day = "1",
language = "English",
isbn = "9783540717881",
volume = "4412 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "170--179",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Ant colony optimization for satellite customer assignment

AU - Kim, S. S.

AU - Kim, Hyong Joong

AU - Mani, V.

AU - Kim, C. H.

PY - 2007/12/1

Y1 - 2007/12/1

N2 - This paper considers the meta-heuristic method of ant colony optimization to the problem of assigning customers to satellite channels. It is shown in an earlier study that finding an optimal allocation of customers to satellite channels is a difficult combinatorial optimization problem and is NP-complete. Hence, we propose an ant colony system (ACS) with strategies of ranking and Max-Min ant system (MMAS) for an effective search of the best/optimal assignment of customers to satellite channels under a dynamic environment. Our simulation results show that this methodology is successful in finding an assignment of customers to satellite channels. Three strategies, ACS with only ranking, ACS with only MMAS, and ACS with both ranking and MMAS are considered. A comparison of these strategies are presented to show the performance of each strategy.

AB - This paper considers the meta-heuristic method of ant colony optimization to the problem of assigning customers to satellite channels. It is shown in an earlier study that finding an optimal allocation of customers to satellite channels is a difficult combinatorial optimization problem and is NP-complete. Hence, we propose an ant colony system (ACS) with strategies of ranking and Max-Min ant system (MMAS) for an effective search of the best/optimal assignment of customers to satellite channels under a dynamic environment. Our simulation results show that this methodology is successful in finding an assignment of customers to satellite channels. Three strategies, ACS with only ranking, ACS with only MMAS, and ACS with both ranking and MMAS are considered. A comparison of these strategies are presented to show the performance of each strategy.

UR - http://www.scopus.com/inward/record.url?scp=38049129501&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=38049129501&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:38049129501

SN - 9783540717881

VL - 4412 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 170

EP - 179

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -