Expensive multiobjective optimization by MOEA/D with Gaussian process model

  • Authors:

  • Qingfu Zhang;Wudong Liu;Edward Tsang;Botond Virginas

  • Affiliations:

  • School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK;School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK;School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK;Intelligent Systems Research Center, BT Exact, Ipswich, UK

  • Venue:
  • IEEE Transactions on Evolutionary Computation
  • Year:
  • 2010

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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/DEGO 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.