Fuzzy simulated evolution algorithm for topology design of campusnetworks

The topology design of campus networks is a hard constrained combinatorial optimization problem. It consists of deciding the number, type, and location of the active network elements (nodes) and links. This choice is dictated by physical and technological constraints and must optimize several object...

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محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Youssef, H. (author)
مؤلفون آخرون: Sait, Sadiq M. (author), Khan, S.A. (author), unknown (author)
التنسيق: article
منشور في: 2000
الموضوعات:
الوصول للمادة أونلاين:https://eprints.kfupm.edu.sa/id/eprint/14850/1/14850_1.pdf
https://eprints.kfupm.edu.sa/id/eprint/14850/2/14850_2.doc
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author Youssef, H.
author2 Sait, Sadiq M.
Khan, S.A.
unknown
author2_role author
author
author
author_facet Youssef, H.
Sait, Sadiq M.
Khan, S.A.
unknown
author_role author
dc.creator.none.fl_str_mv Youssef, H.
Sait, Sadiq M.
Khan, S.A.
unknown
dc.date.none.fl_str_mv 2000
2020
dc.format.none.fl_str_mv application/pdf
application/msword
dc.identifier.none.fl_str_mv https://eprints.kfupm.edu.sa/id/eprint/14850/1/14850_1.pdf
https://eprints.kfupm.edu.sa/id/eprint/14850/2/14850_2.doc
(2000) Fuzzy simulated evolution algorithm for topology design of campusnetworks. Evolutionary Computation, 2000. Proceedings of the 2000 Congress on, 1.
dc.language.none.fl_str_mv en
en
dc.publisher.none.fl_str_mv IEEE
dc.relation.none.fl_str_mv https://eprints.kfupm.edu.sa/id/eprint/14850/
dc.rights.*.fl_str_mv info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv Computer
dc.title.none.fl_str_mv Fuzzy simulated evolution algorithm for topology design of campusnetworks
dc.type.none.fl_str_mv Article
PeerReviewed
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
description The topology design of campus networks is a hard constrained combinatorial optimization problem. It consists of deciding the number, type, and location of the active network elements (nodes) and links. This choice is dictated by physical and technological constraints and must optimize several objectives. Example of objectives are monetary cost, network delay, and hop count between communicating pairs. Furthermore, due to the nondeterministic nature of network traffic and other design parameters, the objective criteria are imprecise. Fuzzy logic provides a suitable mathematical framework in such a situation. We present an approach based on the simulated evolution algorithm for the design of campus network topology. The two main phases of the algorithm, namely, evaluation and allocation, have been fuzzified. To diversify the search, we have also incorporated tabu search-based characteristics in the allocation phase of the SE algorithm. This approach is then compared with the simulated annealing algorithm, which is another well-known heuristic. Results show that on all test cases the simulated evolution algorithm exhibits more intelligent search of the solution subspace and was able to find better solutions than simulated annealing
eu_rights_str_mv openAccess
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id KFUPM_010506731550e270e422404d64f7b66b
identifier_str_mv (2000) Fuzzy simulated evolution algorithm for topology design of campusnetworks. Evolutionary Computation, 2000. Proceedings of the 2000 Congress on, 1.
language_invalid_str_mv en
network_acronym_str KFUPM
network_name_str King Fahd University of Petroleum and Minerals
oai_identifier_str oai::14850
publishDate 2000
publisher.none.fl_str_mv IEEE
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spelling Fuzzy simulated evolution algorithm for topology design of campusnetworksYoussef, H.Sait, Sadiq M.Khan, S.A.unknownComputerThe topology design of campus networks is a hard constrained combinatorial optimization problem. It consists of deciding the number, type, and location of the active network elements (nodes) and links. This choice is dictated by physical and technological constraints and must optimize several objectives. Example of objectives are monetary cost, network delay, and hop count between communicating pairs. Furthermore, due to the nondeterministic nature of network traffic and other design parameters, the objective criteria are imprecise. Fuzzy logic provides a suitable mathematical framework in such a situation. We present an approach based on the simulated evolution algorithm for the design of campus network topology. The two main phases of the algorithm, namely, evaluation and allocation, have been fuzzified. To diversify the search, we have also incorporated tabu search-based characteristics in the allocation phase of the SE algorithm. This approach is then compared with the simulated annealing algorithm, which is another well-known heuristic. Results show that on all test cases the simulated evolution algorithm exhibits more intelligent search of the solution subspace and was able to find better solutions than simulated annealingIEEE20002020ArticlePeerReviewedinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfapplication/mswordhttps://eprints.kfupm.edu.sa/id/eprint/14850/1/14850_1.pdfhttps://eprints.kfupm.edu.sa/id/eprint/14850/2/14850_2.doc (2000) Fuzzy simulated evolution algorithm for topology design of campusnetworks. Evolutionary Computation, 2000. Proceedings of the 2000 Congress on, 1. enenhttps://eprints.kfupm.edu.sa/id/eprint/14850/info:eu-repo/semantics/openAccessoai::148502019-11-01T14:07:44Z
spellingShingle Fuzzy simulated evolution algorithm for topology design of campusnetworks
Youssef, H.
Computer
status_str publishedVersion
title Fuzzy simulated evolution algorithm for topology design of campusnetworks
title_full Fuzzy simulated evolution algorithm for topology design of campusnetworks
title_fullStr Fuzzy simulated evolution algorithm for topology design of campusnetworks
title_full_unstemmed Fuzzy simulated evolution algorithm for topology design of campusnetworks
title_short Fuzzy simulated evolution algorithm for topology design of campusnetworks
title_sort Fuzzy simulated evolution algorithm for topology design of campusnetworks
topic Computer
url https://eprints.kfupm.edu.sa/id/eprint/14850/1/14850_1.pdf
https://eprints.kfupm.edu.sa/id/eprint/14850/2/14850_2.doc