Two_Arch2: An Improved Two-Archive Algorithm for Many-Objective Optimization

Handing Wang, Licheng Jiao, Xin Yao

Research output: Contribution to journalArticlepeer-review

240 Citations (Scopus)


Many-objective optimization problems (ManyOPs) refer, usually, to those multiobjective problems (MOPs) with more than three objectives. Their large numbers of objectives pose challenges to multiobjective evolutionary algorithms (MOEAs) in terms of convergence, diversity, and complexity. Most existing MOEAs can only perform well in one of those three aspects. In view of this, we aim to design a more balanced MOEA on ManyOPs in all three aspects at the same time. Among the existing MOEAs, the two-archive algorithm (Two-Arch) is a low-complexity algorithm with two archives focusing on convergence and diversity separately. Inspired by the idea of Two-Arch, we propose a significantly improved two-archive algorithm (i.e., Two-Arch2) for ManyOPs in this paper. In our Two-Arch2, we assign different selection principles (indicator-based and Pareto-based) to the two archives. In addition, we design a new Lp-norm-based ( p

Original languageEnglish
Article number6883177
Pages (from-to)524-541
Number of pages18
JournalIEEE Transactions on Evolutionary Computation
Issue number4
Publication statusPublished - 1 Aug 2015


  • Evolutionary algorithm
  • Lp-norm
  • manyobjective optimization
  • two-archive algorithm (Two-Arch)

ASJC Scopus subject areas

  • Software
  • Computational Theory and Mathematics
  • Theoretical Computer Science


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