Evolving dynamic multi-objective optimization problems with objective replacement

Sheng Uei Guan*, Qian Chen, Wenting Mo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

46 Citations (Scopus)

Abstract

This paper studies the strategies for multi-objective optimization in a dynamic environment. In particular we focus on problems with objective replacement where some objectives may be replaced with new objectives during evolution. It is shown that the Pareto-optimal sets before and after the objective replacement share some common members. Based on this observation we suggest the inheritance strategy. When objective replacement occurs this strategy selects good chromosomes according to the new objective set from the solutions found before objective replacement and then continues to optimize them via evolution for the new objective set. The experiment results showed that this strategy can help MOGAs achieve better performance than MOGAs without using the inheritance strategy where the evolution is restarted when objective replacement occurs. More solutions with better quality are found during the same time span.

Original languageEnglish
Pages (from-to)267-293
Number of pages27
JournalArtificial Intelligence Review
Volume23
Issue number3
DOIs
Publication statusPublished - May 2005
Externally publishedYes

Keywords

  • Multi-objective genetic algorithms
  • Multi-objective optimization
  • Multi-objective problems
  • Non-stationary environment

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