Paper Info
Title
Solving the GMM-model with a MOEA
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.
Year
Venue
Keywords
2005
CCIA
different objective,multiobjective context,generalized multiobjective multitree model,mathematical solution,multitree-multicast load balancing,multi-objective evolutionary algorithm,whole pareto optimal set,strength pareto evolutionary algorithm,simultaneous data flow,multi objective optimization,load balance,multitree,data flow,local search
Field
DocType
Volume
Mathematical optimization,Evolutionary algorithm,Spea,Load balancing (computing),Computer science,Multitree,Pareto optimal,Multi-objective optimization,Artificial intelligence,Pareto principle
Conference
131
ISSN
ISBN
Citations 
0922-6389
1-58603-560-6
0
PageRank 
References 
Authors
0.34
4
5
Name
Order
Citations
PageRank
F. Solano100.34
R. Fabregat2254.86
B. Barán361.30
Y. Donoso421.41
J. L. Marzo5697.13