Publication: Multiobjective Algorithms Hybridization to Optimize Broadcasting Parameters in Mobile Ad-Hoc Networks
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2009
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Bio-Inspired Systems: Computational and Ambient Intelligence
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Abstract
The aim os this paper is to study the hybridization of two multi-objective algorithms in the context of a real problem, the MANETs problem. The algorithms studied are Particle Swarm Optimization (MOPSO) and a new multiobjective algorithm based in the combination of NSGA-II with Evolution Strategies (ESN). This work analyzes the improvement produced by hybridization over the Pareto’s fronts compared with the non-hybridized algorithms. The purpose of this work is to validate how hybridization of two evolutionary algorithms of different families may help to solve certain problems together in the context of MANETs problem. The hybridization used for this work consists on a sequential execution of the two algorithms and using the final population of the first algorithm as initial population of the second one.
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Proceeding of: 10th InternationalWork-Conference
on Artificial Neural Networks, IWANN 2009
Salamanca, Spain, June 10-12, 2009
Keywords
Mobile ad-hoc networks, ESN, MANETs, MOPSO, Hybridation, Multiobjective optimization problem
Bibliographic citation
Bio-Inspired Systems: Computational and Ambient Intelligence: 10th InternationalWork-Conference
on Artificial Neural Networks, IWANN 2009
Salamanca, Spain, June 10-12, 2009
Proceedings, Part I. Springer, 2009, pp.728-735