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Expensive multiobjective optimization by MOEA/D with gaussian process model

Zhang, Q and Liu, W and Tsang, E and Virginas, B (2010) 'Expensive multiobjective optimization by MOEA/D with gaussian process model.' IEEE Transactions on Evolutionary Computation, 14 (3). 456 - 474. ISSN 1089-778X

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Abstract

In some expensive multiobjective optimization problems (MOPs), several function evaluations can be carried out in a batch way. Therefore, it is very desirable to develop methods which can generate multipler test points simultaneously. This paper proposes such a method, called MOEA/D-EGO, for dealing with expensive multiobjective optimization. MOEA/D-EGO decomposes an MOP in question into a number of single-objective optimization subproblems. A predictive model is built for each subproblem based on the points evaluated so far. Effort has been made to reduce the overhead for modeling and to improve the prediction quality. At each generation, MOEA/D is used for maximizing the expected improvement metric values of all the subproblems, and then several test points are selected for evaluation. Extensive experimental studies have been carried out to investigate the ability of the proposed algorithm. © 2006 IEEE.

Item Type: Article
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 13 Jan 2012 11:26
Last Modified: 30 Jan 2019 16:16
URI: http://repository.essex.ac.uk/id/eprint/1971

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