Online objective reduction for many-objective optimization problems

Yiu Ming CHEUNG*, Fangqing Gu

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

10 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1165-1171
Number of pages7
ISBN (Electronic)9781479914883
DOIs
Publication statusPublished - 16 Sep 2014
Event2014 IEEE Congress on Evolutionary Computation, CEC 2014 - Beijing, China
Duration: 6 Jul 201411 Jul 2014

Publication series

NameProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014

Conference

Conference2014 IEEE Congress on Evolutionary Computation, CEC 2014
Country/TerritoryChina
CityBeijing
Period6/07/1411/07/14

Scopus Subject Areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Theoretical Computer Science

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