Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

Online objective reduction for many-objective optimization problems

Language

English

Abstract

© 2014 IEEE.For many-objective optimization problems, i.e. the number of objectives is greater than three, the performance of most of the existing Evolutionary Multi-objective Optimization algorithms will deteriorate to a certain degree. It is therefore desirable to reduce many objectives to fewer essential objectives, if applicable. Currently, most of the existing objective reduction methods are based on objective selection, whose computational process is, however, laborious. In this paper, we will propose an online objective reduction method based on objective extraction for the many-objective optimization problems. It formulates the essential objective as a linear combination of the original objectives with the combination weights determined based on the correlations of each pair of the essential objectives. Subsequently, we will integrate it into NSGA-II. Numerical studies have show the efficacy of the proposed approach.

Publication Date

2014

Source Publication Title

2014 IEEE Congress on Evolutionary Computation (CEC)

Start Page

1165

End Page

1171

Conference Location

Beijing, China

Publisher

IEEE

DOI

10.1109/CEC.2014.6900548

Link to Publisher's Edition

http://dx.doi.org/10.1109/CEC.2014.6900548

ISBN (print)

9781479966264

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