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

Miqing LI*, Xin YAO

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

29 Citations (Scopus)


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. © Springer Nature Switzerland AG 2019.
Original languageEnglish
Title of host publicationEvolutionary Multi-Criterion Optimization : 10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings
EditorsKalyanmoy DEB, Erik GOODMAN, Carlos A. Coello COELLO, Kathrin KLAMROTH, Kaisa MIETTINEN, Sanaz MOSTAGHIM, Patrick REED
Number of pages12
ISBN (Electronic)9783030125981
ISBN (Print)9783030125974
Publication statusPublished - 2019
Externally publishedYes
Event10th International Conference on Evolutionary Multi-Criterion Optimization - East Lansing, United States
Duration: 10 Mar 201913 Mar 2019

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameTheoretical Computer Science and General Issues
ISSN (Print)2512-2010
ISSN (Electronic)2512-2029


Conference10th International Conference on Evolutionary Multi-Criterion Optimization
Abbreviated titleEMO 2019
Country/TerritoryUnited States
CityEast Lansing

Bibliographical note

This work was supported by EPSRC (Grant Nos. EP/J017515/1 and EP/P005578/1) and Science and Technology Innovation Committee Foundation of Shenzhen (Grant Nos. ZDSYS201703031748284 and JCYJ20170307105521943).


  • Archive
  • Empirical investigation
  • Evolutionary computation
  • Monotonicity
  • Multi-objective optimisation
  • Optimality


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