Solving the GMM-model with a MOEA

Authors:
F. Solano;R. Fabregat;B. Barán;Y. Donoso;J. L. Marzo
Affiliations:
IIiA. University of Girona (Spain), {ramon, fsolanod, marzo}@eia.udg.es;IIiA. University of Girona (Spain), {ramon, fsolanod, marzo}@eia.udg.es;National University of Asuncion (Paraguay), bbaran@cnc.una.py;Universidad del Norte (Colombia), ydonoso@uninorte.edu.co;IIiA. University of Girona (Spain), {ramon, fsolanod, marzo}@eia.udg.es
Venue:
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
Year:
2005

Citing 4
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Abstract

The Generalized Multiobjective Multitree model (GMM-model) considers, for the first time, multitree-multicast load balancing with splitting in a multiobjective context. The mathematical solution of the GMM-model is a whole Pareto optimal set that includes several previously published results, according to surveyed publications. To solve the GMM-model, this paper proposes a multi-objective evolutionary algorithm (MOEA) inspired by the Strength Pareto Evolutionary Algorithm (SPEA). Experimental results considering up to 11 different objectives are presented for the well-known NSF network, with two simultaneous data flows.