Techniques for highly multiobjective optimisation: Some nondominated points are better than others

David W. Corne, Joshua D. Knowles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

214 Citations (Scopus)

Abstract

The research area of evolutionary multiobjective optimization (EMO) is reaching better understandings of the properties and capabilities of EMO algorithms, and accumulating much evidence of their worth in practical scenarios. An urgent emerging issue is that the favoured EMO algorithms scale poorly when problems have "many" (e.g. five or more) objectives. One of the chief reasons for this is believed to be that, in many-objective EMO search, populations are likely to be largely composed of nondominated solutions. In turn, this means that the commonly-used algorithms cannot distinguish between these for selective purposes. However, there are methods that can be used validly to rank points in a nondominated set, and may therefore usefully underpin selection in EMO search. Here we discuss and compare several such methods. Our main finding is that simple variants of the often-overlooked "Average Ranking" strategy usually outperform other methods tested, covering problems with 5-20 objectives and differing amounts of inter-objective correlation. Copyright 2007 ACM.

Original languageEnglish
Title of host publicationProceedings of GECCO 2007: Genetic and Evolutionary Computation Conference
Pages773-780
Number of pages8
DOIs
Publication statusPublished - 2007
Event9th Annual Genetic and Evolutionary Computation Conference - London, United Kingdom
Duration: 7 Jul 200711 Jul 2007

Conference

Conference9th Annual Genetic and Evolutionary Computation Conference
Abbreviated titleGECCO 2007
CountryUnited Kingdom
CityLondon
Period7/07/0711/07/07

Keywords

  • Multiobjective optimization
  • Ranking
  • Selection

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