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

Karshenas, Hossein and Santana, Roberto and Bielza, Concha and Larrañaga Múgica, Pedro (2012). Multi-objective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables. Monografía (Technical Report). Facultad de Informática (UPM).

Description

Title: Multi-objective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables
Author/s:
  • Karshenas, Hossein
  • Santana, Roberto
  • Bielza, Concha
  • Larrañaga Múgica, Pedro
Item Type: Monograph (Technical Report)
Date: December 2012
Subjects:
Freetext Keywords: Estimation of distribution algorithm, Joint objective-variable modeling, Many-objective problem, Multiobjective optimization, Objectives relationship
Faculty: Facultad de Informática (UPM)
Department: Inteligencia Artificial
UPM's Research Group: Computational Intelligence Group
Creative Commons Licenses: None

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Abstract

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.

More information

Item ID: 14321
DC Identifier: http://oa.upm.es/14321/
OAI Identifier: oai:oa.upm.es:14321
Deposited by: Catedrática de Universidad Concha Bielza Lozoya
Deposited on: 21 Jan 2013 08:17
Last Modified: 21 Apr 2016 13:56
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