Two-Archive Evolutionary Algorithm for Constrained Multiobjective Optimization

Ke LI, Renzhi CHEN, Guangtao FU, Xin YAO

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

332 Citations (Scopus)


When solving constrained multiobjective optimization problems, an important issue is how to balance convergence, diversity, and feasibility simultaneously. To address this issue, this paper proposes a parameter-free constraint handling technique, a two-archive evolutionary algorithm, for constrained multiobjective optimization. It maintains two collaborative archives simultaneously: one, denoted as the convergence-oriented archive (CA), is the driving force to push the population toward the Pareto front; the other one, denoted as the diversity-oriented archive (DA), mainly tends to maintain the population diversity. In particular, to complement the behavior of the CA and provide as much diversified information as possible, the DA aims at exploring areas under-exploited by the CA including the infeasible regions. To leverage the complementary effects of both archives, we develop a restricted mating selection mechanism that adaptively chooses appropriate mating parents from them according to their evolution status. Comprehensive experiments on a series of benchmark problems and a real-world case study fully demonstrate the competitiveness of our proposed algorithm, in comparison to five state-of-the-art constrained evolutionary multiobjective optimizers. © 1997-2012 IEEE.
Original languageEnglish
Article number8413136
Pages (from-to)303-315
Number of pages13
JournalIEEE Transactions on Evolutionary Computation
Issue number2
Early online date19 Jul 2018
Publication statusPublished - Apr 2019
Externally publishedYes

Bibliographical note

This work was supported in part by the Royal Society under Grant IEC/NSFC/170243, in part by the Ministry of Science and Technology of China under Grant 2017YFC0804003, in part by the Science and Technology Innovation Committee Foundation of Shenzhen under Grant ZDSYS201703031748284, in part by the Shenzhen Peacock Plan under Grant KQTD2016112514355531, and in part by EPSRC under Grant EP/J017515/1 and Grant EP/P005578/1. The work of G. Fu was supported by the Royal Society Industry Fellowship under Grant IF160108. (Ke Li and Renzhi Chen contributed equally to this work.)


  • Constraint handling
  • decomposition-based technique
  • evolutionary algorithm (EA)
  • multiobjective optimization
  • two-archive strategy


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