  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, two-archive evolutionary algorithm, for constrained multi-objective
optimization. It maintains two co-evolving populations simultaneously: one,
denoted as convergence archive, is the driving force to push the population
toward the Pareto front; the other one, denoted as diversity archive, mainly
tends to maintain the population diversity. In particular, to complement the
behavior of the convergence archive and provide as much diversified information
as possible, the diversity archive aims at exploring areas under-exploited by
the convergence archive 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, comparing to five state-of-the-art
constrained evolutionary multi-objective optimizers.
