Adaptive, convergent, and diversified archiving strategy for multiobjective evolutionary algorithms

Huidong JIN, Man Leung WONG

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

7 Citations (Scopus)


It is crucial to obtain automatically and efficiently a well-distributed set of Pareto optimal solutions in multiobjective evolutionary algorithms (MOEAs). Many studies have proposed different evolutionary algorithms that can progress toward the Pareto front with a widely spread distribution of solutions. However, most theoretically, convergent MOEAs necessitate certain prior knowledge about the Pareto front in order to efficiently maintain widespread solutions. In this paper, we propose, based on the new E-dominance concept, an Adaptive Rectangle Archiving (ARA) strategy that overcomes this important problem. The MOEA with this archiving technique provably converges to well-distributed Pareto optimal solutions without prior knowledge about the Pareto front. ARA complements the existing archiving techniques and is useful to both researchers and practitioners.
Original languageEnglish
Pages (from-to)8462-8470
Number of pages9
JournalExpert Systems with Applications
Issue number12
Publication statusPublished - 1 Dec 2010


  • Evolutionary computation
  • convergence
  • diversified archiving
  • multiobjective evolutionary algorithms
  • ϵ-Pareto set


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