Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/38300

TitleComparison of single and multi-objective evolutionary algorithms for robust link-state routing
Author(s)Pereira, Vítor
Sousa, Pedro
Cortez, Paulo
Rio, Miguel
Rocha, Miguel
KeywordsMulti-objective evolutionary algorithms
Traffic Engineering
NSGA
SPEA
intra-domain routing
OSPF
Issue dateMar-2015
PublisherSpringer Verlag
JournalLecture Notes in Computer Science
CitationPereira, V., Sousa, P., Cortez, P., Rio, M., & Rocha, M. (2015) Comparison of single and multi-objective evolutionary algorithms for robust link-state routing. Vol. 9019. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 573-587).
Abstract(s)Traffic Engineering (TE) approaches are increasingly impor- tant in network management to allow an optimized configuration and resource allocation. In link-state routing, the task of setting appropriate weights to the links is both an important and a challenging optimization task. A number of different approaches has been put forward towards this aim, including the successful use of Evolutionary Algorithms (EAs). In this context, this work addresses the evaluation of three distinct EAs, a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to mini- mize network congestion. In both tasks, the optimization considers sce- narios where there is a dynamic alteration in the state of the system, in the first considering changes in the traffic demand matrices and in the latter considering the possibility of link failures. The methods will, thus, need to simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach towards robust configurations. Since this can be formulated as a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came nat- urally, being those compared to a single-objective EA. The results show a remarkable behavior of NSGA-II in all proposed tasks scaling well for harder instances, and thus presenting itself as the most promising option for TE in these scenarios.
TypeConference paper
URIhttp://hdl.handle.net/1822/38300
ISBN978-3-319-15891-4
DOI10.1007/978-3-319-15892-1_39
ISSN0302-9743
Publisher versionThe original publication is available at http://link.springer.com/chapter/10.1007/978-3-319-15892-1_39
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series
CAlg - Artigos em livros de atas/Papers in proceedings

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