Two-Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization

Ke Li, Renzhi Chen, Guangtao Fu, Xin Yao

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)
182 Downloads (Pure)


When solving constrained multi-objective 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 multi-objective 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 multi-objective optimizers.
Original languageEnglish
Pages (from-to)1-1
JournalIEEE Transactions on Evolutionary Computation
Early online date19 Jul 2018
Publication statusE-pub ahead of print - 19 Jul 2018


  • Multi-objective optimization
  • constraint handling
  • evolutionary algorithm
  • two-archive strategy


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