An empirical investigation of the optimality and monotonicity properties of multiobjective archiving methods

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Abstract

Most evolutionary multiobjective optimisation (EMO) algorithms explicitly or implicitly maintain an archive for an approximation of the Pareto front. A question arising is whether existing archiving methods are reliable with respect to their convergence and approximation ability. Despite theoretical results available, it remains unknown how these archivers actually perform in practice. In particular, what percentage of solutions in their final archive are Pareto optimal? How frequently do they experience deterioration during the archiving process? Deterioration means archiving a new solution which is dominated by some solution discarded previously. This paper answers the above questions through a systematic investigation of eight representative archivers on 37 test instances with two to five objectives. We have found that 1) deterioration happens to all the archivers; 2) the deterioration degree can vary dramatically on different problems; 3) some archivers clearly perform better than others; and 4) several popular archivers sometime return a population with most solutions being the non-optimal. All of these suggest the need of improvement of current archiving methods.
Original languageEnglish
Title of host publicationEvolutionary Multi-Criterion Optimization
Subtitle of host publication10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings
EditorsKalyanmoy Deb, Erik Goodman, Carlos Coello Coello, Kathrin Klamroth, Kaisa Miettinen, Sanaz Mostaghim, Patrick Reed
PublisherSpringer
Pages15-26
Number of pages12
ISBN (Electronic)978-3-030-12598-1
ISBN (Print)978-3-030-12597-4
DOIs
Publication statusE-pub ahead of print - 3 Feb 2019
Event10th International Conference on Evolutionary Multi-Criterion Optimization, (EMO 19) - East Lansing, Michigan, United States
Duration: 10 Mar 201913 Mar 2019

Publication series

NameLecture Notes in Computer Science - Theoretical Computer Science and General Issues
PublisherSpringer
Volume11411
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Evolutionary Multi-Criterion Optimization, (EMO 19)
Country/TerritoryUnited States
CityEast Lansing, Michigan
Period10/03/1913/03/19

Keywords

  • multi-objective optimisation
  • archive
  • optimality
  • monotonicity
  • empirical investigation
  • evolutionary computation

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