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Memorias de investigación
Artículos en revistas:
Interval-based ranking in noisy evolutionary multiobjective optimization
Año:2014
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
As one of the most competitive approaches to multi-objective optimization, evolutionary algorithms have been shown to obtain very good results for many realworld multi-objective problems. One of the issues that can affect the performance of these algorithms is the uncertainty in the quality of the solutions which is usually represented with the noise in the objective values. Therefore, handling noisy objectives in evolutionary multi-objective optimization algorithms becomes very important and is gaining more attention in recent years. In this paper we present ?-degree Pareto dominance relation for ordering the solutions in multi-objective optimization when the values of the objective functions are given as intervals. Based on this dominance relation, we propose an adaptation of the non-dominated sorting algorithm for ranking the solutions. This ranking method is then used in a standardmulti-objective evolutionary algorithm and a recently proposed novel multi-objective estimation of distribution algorithm based on joint variable-objective probabilistic modeling, and applied to a set of multi-objective problems with different levels of independent noise. The experimental results show that the use of the proposed method for solution ranking allows to approximate Pareto sets which are considerably better than those obtained when using the dominance probability-based ranking method, which is one of the main methods for noise handling in multi-objective optimization.
Internacional
Si
JCR del ISI
Si
Título de la revista
Computational Optimization And Applications
ISSN
0926-6003
Factor de impacto JCR
1,278
Información de impacto
Datos JCR del año 2012
Volumen
aceptado
DOI
DOI 10.1007/s10589-014-9717-1
Número de revista
Desde la página
3
Hasta la página
41
Mes
SIN MES
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Participantes
  • Autor: Hossein Karshenas Najafabadi (UPM)
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
  • Autor: Pedro Maria Larrañaga Mugica (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Computational Intelligence Group
  • Departamento: Inteligencia Artificial
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