  Constrained multi-objective optimization problems (CMOPs) are ubiquitous in
real-world engineering optimization scenarios. A key issue in constrained
multi-objective optimization is to strike a balance among convergence,
diversity and feasibility. A recently proposed two-archive evolutionary
algorithm for constrained multi-objective optimization (C-TAEA) has be shown as
a latest algorithm. However, due to its simple implementation of the
collaboration mechanism between its two co-evolving archives, C-TAEA is
struggling when solving problems whose \textit{pseudo} Pareto-optimal front,
which does not take constraints into consideration, dominates the
\textit{feasible} Pareto-optimal front. In this paper, we propose an improved
version C-TAEA, dubbed C-TAEA-II, featuring an improved update mechanism of two
co-evolving archives and an adaptive mating selection mechanism to promote a
better collaboration between co-evolving archives. Empirical results
demonstrate the competitiveness of the proposed C-TAEA-II in comparison with
five representative constrained evolutionary multi-objective optimization
algorithms.
