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A comparison of multi-objective optimization algorithms for weight setting problems in traffic engineering

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Abstract

Traffic engineering approaches are increasingly important in network management to allow an optimized configuration and resource allocation. In link-state routing, setting appropriate weights to the links is an important and challenging optimization task. Different approaches have been put forward towards this aim, including evolutionary algorithms (EAs). This work addresses the evaluation of 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 minimize network congestion. In both tasks, the optimization considers scenarios where there is a dynamic alteration in the network, with (1) changes in the traffic demand matrices, and (2) link failures. The methods will simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach. Since this leads to a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came naturally; those are compared to a single-objective EA previously proposed by the authors. The results show a remarkable performance and scalability of NSGA-II in the proposed tasks presenting itself as the most promising option for TE.

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Acknowledgements

This work has been supported by FCT-Fundação para a Ciência e Tecnologia within the R&D Units Project Scope UIDB/00319/2020 and UIDB/04469/2020 units.

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Authors and Affiliations

  1. Department of Informatics, Centre of Biological Engineering, University of Minho, Braga, Portugal

    Vítor Pereira & Miguel Rocha

  2. Department of Informatics, Centro Algoritmi, University of Minho, Braga, Portugal

    Pedro Sousa

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  1. Vítor Pereira
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  2. Pedro Sousa
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  3. Miguel Rocha
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Correspondence to Vítor Pereira.

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Pereira, V., Sousa, P. & Rocha, M. A comparison of multi-objective optimization algorithms for weight setting problems in traffic engineering. Nat Comput 21, 507–522 (2022). https://doi.org/10.1007/s11047-020-09807-1

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