Variable neighborhood multiobjective genetic algorithm for the optimization of routes on IP networks

Authors:
Renata E. Onety;Gladston J. P. Moreira;Oriane M. Neto;Ricardo H. C. Takahashi
Affiliations:
Universidade Federal de Minas Gerais, School of Electrical Engineering, Belo Horizonte, MG, Brazil;Universidade Federal dos Vales do Jequitinhonha e Mucuri, Teófile Otoni, MG, Brazil;Universidade Federal de Minas Gerais, School of Electrical Engineering, Belo Horizonte, MG, Brazil;Universidade Federal de Minas Gerais, Dep. of Mathematics, Belo Horizonte, MG, Brazil
Venue:
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Year:
2011

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Abstract

This paper proposes an algorithm to optimize multiple indices of Quality of Service of Multi Protocol Label Switching (MPLS) IP networks. The proposed algorithm, the Variable Neighborhood Multiobjective Genetic Algorithm (VN-MGA), is a Genetic Algorithm based on the NSGA-II, with the particular feature that different parts of a solution are encoded differently, at Level 1 and Level 2. In order to improve the results, both representations are needed. At Level 1, the first part of the solution is encoded, by considering as decision variables, the arrows that form the routes to be followed by each request (whilst the second part of the solution is kept constant), whereas at Level 2, the second part of the solution is encoded, by considering as decision variables, the sequence of requests, and first part is kept constant. The preliminary results shown here indicate that the proposed approach is promising, since the Pareto-fronts obtained by VN-MGA dominate the fronts obtained by fixed-neighborhood encoding schemes. Besides the potential benefits of the application of the proposed approach to the optimization of packet routing in MPLS networks, this work raises the theoretical issue of the systematic application of variable encodings, which allow variable neighborhood searches, as generic operators inside general evolutionary computation algorithms.