Many-objective evolutionary algorithms: A survey

Bingdong LI, Jinlong LI, Ke TANG, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

629 Citations (Scopus)

Abstract

Multiobjective evolutionary algorithms (MOEAs) have been widely used in real-world applications. However, most MOEAs based on Pareto-dominance handle many-objective problems (MaOPs) poorly due to a high proportion of incomparable and thus mutually nondominated solutions. Recently, a number ofmany-objective evolutionary algorithms (MaOEAs) have been proposed to deal with this scalability issue. In this article, a survey of MaOEAs is reported. According to the key ideas used, MaOEAs are categorized into seven classes: relaxed dominance based, diversity-based, aggregation-based, indicator-based, reference set based, preference-based, and dimensionality reduction approaches. Several future research directions in this field are also discussed.
Original languageEnglish
Article numberA10
JournalACM Computing Surveys
Volume48
Issue number1
DOIs
Publication statusPublished - 29 Sept 2015
Externally publishedYes

Keywords

  • Evolutionary algorithm
  • Many-objective optimization
  • Scalability

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