A Review of Surrogate Assisted Multiobjective Evolutionary Algorithms

Multiobjective evolutionary algorithms have incorporated surrogate models in order to reduce the number of required evaluations to approximate the Pareto front of computationally expensive multiobjective optimization problems. Currently, few works have reviewed the state of the art in this topic. Ho...

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Bibliographic Details
Published inComputational Intelligence and Neuroscience Vol. 2016; no. 2016; pp. 987 - 1000
Main Authors Alan Díaz-Manríquez, Gregorio Toscano, Jose Hugo Barron-Zambrano, Edgar Tello-Leal
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Limiteds 01.01.2016
Hindawi Publishing Corporation
Hindawi Limited
Subjects
Accuracy
Algorithms
Approximation
Biological Evolution
Classification
Computation
Computer Simulation
Engineering
Evolutionary algorithms
Genetic algorithms
Humans
Intelligence
Models, Theoretical
Optimization
Optimization algorithms
Pareto optimality
Review
State of the art
Taxonomy
Vocabularies & taxonomies
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Summary:Multiobjective evolutionary algorithms have incorporated surrogate models in order to reduce the number of required evaluations to approximate the Pareto front of computationally expensive multiobjective optimization problems. Currently, few works have reviewed the state of the art in this topic. However, the existing reviews have focused on classifying the evolutionary multiobjective optimization algorithms with respect to the type of underlying surrogate model. In this paper, we center our focus on classifying multiobjective evolutionary algorithms with respect to their integration with surrogate models. This interaction has led us to classify similar approaches and identify advantages and disadvantages of each class.
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Academic Editor: Manuel Graña
ISSN:1687-5265
1687-5273
DOI:10.1155/2016/9420460