Multiobjective Algorithms Hybridization to Optimize Broadcasting Parameters in Mobile Ad-Hoc Networks

Proceeding of: 10th InternationalWork-Conference on Artificial Neural Networks, IWANN 2009 Salamanca, Spain, June 10-12, 2009The 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.This article has been financed by the Spanish founded research MEC projects OPLINK::UC3M, Ref:TIN2005-08818- C04-02 and MSTAR::UC3M, Ref:TIN2008-06491-C04-03.Publicad

GOM: New Genetic Optimizing Model for broadcasting tree in MANET

Data broadcasting in a mobile ad-hoc network (MANET) is the main method of information dissemination in many applications, in particular for sending critical information to all hosts. Finding an optimal broadcast tree in such networks is a challenging task due to the broadcast storm problem. The aim of this work is to propose a new genetic model using a fitness function with the primary goal of finding an optimal broadcast tree. Our new method, called Genetic Optimisation Model (GOM) alleviates the broadcast storm problem to a great extent as the experimental simulations result in efficient broadcast tree with minimal flood and minimal hops. The result of this model also shows that it has the ability to give different optimal solutions according to the nature of the network. © 2010 IEEE

Algoritmos genéticos para la resolución del problema de BCI

Este proyecto se encuentra situado en el campo de la inteligencia artificial, dentro de la rama de aprendizaje automático. El objetivo ha sido desarrollar un programa que sea capaz de clasificar correctamente los patrones de datos obtenidos a partir de un sistema BCI (Brain Computer Interface). Por medio de este sistema, se obtienen 96 señales eléctricas agrupadas en 8 canales que son producidas por el cerebro del usuario en el momento en el que éste intenta realizar una de las tres acciones requeridas. Estas señales serán preprocesadas y almacenadas en ficheros por medios externos a nuestro programa. Nuestro sistema está constituido por un algoritmo genético simple en cuya función fitness utiliza un clasificador formado por perceptrones simples. La tarea principal de la clasificación de patrones es realizada por el clasificador de perceptrones simples los cuales utilizan las 96 señales eléctricas y sus operaciones con el fin de introducir datos no lineales a los perceptrones simples. El algoritmo genético se ocupará de seleccionar el conjunto de atributos más válidos. La experimentación demuestra que esta aproximación es capaz de clasificar con el mínimo error posible los patrones de datos obtenidos del BCI desechando en cada ronda del algoritmo los datos que no son útiles o que introducen ruido. _____________________________________________________________________________________________________________________This project lies within the artificial intelligence field, in the branch of machine learning. The aim has been to develop a programme that is able to correctly classify the data patterns obtained from a BCI (Brain Computer Interface) system. By means of this system, 96 electric signals are obtained, grouped into 8 channels that are produced by the user‟s brain in the moment he or she tries to carry out one of the three required actions. These signals will be pre-processed and stored in files through external applications to this programme. Our system is designed using a simple genetic algorithm whose fitness function uses a pattern classifier formed by simple perceptrons. The main task of the pattern classification is carried out by simple perceptron classifiers, which use the 96 electric signals and their operations in order to introduce non-linear data to the simple perceptrons. The genetic algorithm will select the collection of most valid attributes. Experimentation has proved that this approximation is able to classify the data patterns obtained from the BCI with the minimum error possible, rejecting in each round of the algorithm the data which is not useful or which introduces some noise.Ingeniería Técnica en Informática de Gestió