http://hdl.handle.net/10016/3857 Thu, 20 Jun 2013 03:57:19 GMT 2013-06-20T03:57:19Z http://hdl.handle.net/10016/16040 Title: Optimizing the DFCN Broadcast Protocol with a Parallel Cooperative Strategy of Multi-Objective Evolutionary Algorithms Author(s): Segura, Carlos; Cervantes, Alejandro; Nebro, Antonio J.; Jaraíz-Simón, María Dolores; Segredo, Eduardo; García-Rodríguez, Sandra; Luna, Francisco; Gómez-Pulido, Juan A.; Miranda, Gara; Luque, Cristóbal; Alba, Enrique; Vega-Rodríguez, Miguel A.; León, Coromoto; Galván, Inés M. Abstract: This work presents the application of a parallel coopera- tive optimization approach to the broadcast operation in mobile ad-hoc networks (manets). The optimization of the broadcast operation im- plies 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 sce- nario. The cooperation of a team of multi-objective evolutionary al- gorithms 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 manets scenario, representing a mall, demon- strate the validity of the new proposed approach. Description: Proceeding of: 5th International Conference, EMO 2009, Nantes, France, April 7-10, 2009 Wed, 31 Dec 2008 23:00:00 GMT http://hdl.handle.net/10016/16040 2008-12-31T23:00:00Z http://hdl.handle.net/10016/15651 Title: Hibridación de dos algoritmos evolutivos para la optimización de funciones multiobjetivo: MOPSO y ESN Author(s): García-Rodríguez, Sandra; Galván, Inés M. Abstract: El presente trabajo de investigación tiene como objetivo estudiar la hibridación de dos algoritmos multiobjetivo: enjambres de partículas (MOPSO) y un algoritmo multiobjetivo basado en la combinación de NSGA-II con Estrategias Evolutivas (ESN). Se pretende analizar si la hibridación permite obtener frentes de Pareto mejores que los obtenidos individualmente por los algoritmos ya que, en estudios previos sobre estos algoritmos, se observó que, para ciertos problemas, un algoritmo puede ayudar a otro (y viceversa) en la obtención de frentes más óptimos. Una forma de plantear esta hibridación es utilizar la población obtenida por un algoritmo para inicializar el otro y, para ello, se han realizado experimentos ejecutados de manera homogénea, para cada una de las aproximaciones así como para la hibridación de ambas, con cuatro funciones teóricas (ZDT1, ZDT2, ZDT3 y ZDT4) y un problema real: MANETs. Description: Actas de: VI Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB'09), Málaga, 11 a 13 de Febrero de 2009 Sat, 31 Jan 2009 23:00:00 GMT http://hdl.handle.net/10016/15651 2009-01-31T23:00:00Z http://hdl.handle.net/10016/15638 Title: Multiobjective Algorithms Hybridization to Optimize Broadcasting Parameters in Mobile Ad-Hoc Networks Author(s): García-Rodríguez, Sandra; Luque, Cristóbal; Cervantes, Alejandro; Galván, Inés M. 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. Description: Proceeding of: 10th InternationalWork-Conference on Artificial Neural Networks, IWANN 2009 Salamanca, Spain, June 10-12, 2009 Wed, 31 Dec 2008 23:00:00 GMT http://hdl.handle.net/10016/15638 2008-12-31T23:00:00Z http://hdl.handle.net/10016/15636 Title: Portfolio Optimization Using SPEA2 with Resampling Author(s): García-Rodríguez, Sandra; Quintana, David; Galván, Inés M.; Isasi, Pedro Abstract: The subject of financial portfolio optimization under real-world constraints is a difficult problem that can be tackled using multiobjective evolutionary algorithms. One of the most problematic issues is the dependence of the results on the estimates for a set of parameters, that is, the robustness of solutions. These estimates are often inaccurate and this may result on solutions that, in theory, offered an appropriate risk/return balance and, in practice, resulted being very poor. In this paper we suggest that using a resampling mechanism may filter out the most unstable. We test this idea on real data using SPEA2 as optimization algorithm and the results show that the use of resampling increases significantly the reliability of the resulting portfolios. Description: Proceeding of: Intelligent Data Engineering and Automated Learning – IDEAL 2011: 12th International Conference, Norwich, UK, September 7-9, 2011 Fri, 31 Dec 2010 23:00:00 GMT http://hdl.handle.net/10016/15636 2010-12-31T23:00:00Z