Multi-objective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables

Karshenas, Hossein; Santana, Roberto; Bielza, Concha y Larrañaga Múgica, Pedro (2012). Multi-objective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables. Monografía (Informe Técnico). Facultad de Informática (UPM) [antigua denominación].

Descripción

Título: Multi-objective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables
Autor/es:
  • Karshenas, Hossein
  • Santana, Roberto
  • Bielza, Concha
  • Larrañaga Múgica, Pedro
Tipo de Documento: Monográfico (Informes, Documentos de trabajo, etc.) (Informe Técnico)
Fecha: Diciembre 2012
Materias:
Palabras Clave Informales: Estimation of distribution algorithm, Joint objective-variable modeling, Many-objective problem, Multiobjective optimization, Objectives relationship
Escuela: Facultad de Informática (UPM) [antigua denominación]
Departamento: Inteligencia Artificial
Grupo Investigación UPM: Computational Intelligence Group
Licencias Creative Commons: Ninguna

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Resumen

This paper proposes a new multi-objective estimation of distribution algorithm (EDA) based on joint modeling of objectives and variables. This EDA uses the multi-dimensional Bayesian network as its probabilistic model. In this way it can capture the dependencies between objectives, variables and objectives, as well as the dependencies learnt between variables in other Bayesian network-based EDAs. This model leads to a problem decomposition that helps the proposed algorithm to find better trade-off solutions to the multi-objective problem. In addition to Pareto set approximation, the algorithm is also able to estimate the structure of the multi-objective problem. To apply the algorithm to many-objective problems, the algorithm includes four different ranking methods proposed in the literature for this purpose. The algorithm is applied to the set of walking fish group (WFG) problems, and its optimization performance is compared with an evolutionary algorithm and another multi-objective EDA. The experimental results show that the proposed algorithm performs significantly better on many of the problems and for different objective space dimensions, and achieves comparable results on some compared with the other algorithms.

Más información

ID de Registro: 14321
Identificador DC: http://oa.upm.es/14321/
Identificador OAI: oai:oa.upm.es:14321
Depositado por: Catedrática de Universidad Concha Bielza Lozoya
Depositado el: 21 Ene 2013 08:17
Ultima Modificación: 21 Abr 2016 13:56
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