Optimizing the DFCN Broadcast Protocol with a Parallel Cooperative Strategy of Multi-Objective Evolutionary Algorithms

  • Authors:

  • Carlos Segura;Alejandro Cervantes;Antonio J. Nebro;María Dolores Jaraíz-Simón;Eduardo Segredo;Sandra García;Francisco Luna;Juan Antonio Gómez-Pulido;Gara Miranda;Cristóbal Luque;Enrique Alba;Miguel Ángel Vega-Rodríguez;Coromoto León;Inés M. Galván

  • Affiliations:

  • Department of Statistics, O.R. and Computation, University of La Laguna,;Computer Science Department, University Carlos III of Madrid,;Computer Science Department, University of Málaga,;Department of Technologies of Computers and Communications, University of Extremadura,;Department of Statistics, O.R. and Computation, University of La Laguna,;Computer Science Department, University Carlos III of Madrid,;Computer Science Department, University of Málaga,;Department of Technologies of Computers and Communications, University of Extremadura,;Department of Statistics, O.R. and Computation, University of La Laguna,;Computer Science Department, University Carlos III of Madrid,;Computer Science Department, University of Málaga,;Department of Technologies of Computers and Communications, University of Extremadura,;Department of Statistics, O.R. and Computation, University of La Laguna,;Computer Science Department, University Carlos III of Madrid,

  • Venue:
  • EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
  • Year:
  • 2009

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Abstract

This work presents the application of a parallel cooperative optimization approach to the broadcast operation in mobile ad-hoc networks (manet s). The optimization of the broadcast operation implies satisfying several objectives simultaneously, so a multi-objective approach has been designed. The optimization lies on searching the best configurations of the dfcn broadcast protocol for a given manet scenario. The cooperation of a team of multi-objective evolutionary algorithms has been performed with a novel optimization model. Such model is a hybrid parallel algorithm that combines a parallel island-based scheme with a hyperheuristic approach. Results achieved by the algorithms in different stages of the search process are analyzed in order to grant more computational resources to the most suitable algorithms. The obtained results for a manet s scenario, representing a mall, demonstrate the validity of the new proposed approach.