  To solve real-world expensive constrained multi-objective optimization
problems (ECMOPs), surrogate/approximation models are commonly incorporated in
evolutionary algorithms to pre-select promising candidate solutions for
evaluation. However, the performance of existing approaches are highly
dependent on the relative position of unconstrained and constrained Pareto
fronts (UPF and CPF, respectively). In addition, the uncertainty information of
surrogate models is often ignored, which can misguide the search. To mitigate
these key issues (among others), an efficient probabilistic selection based
constrained multi-objective EA is proposed, referred to as PSCMOEA. It
comprises novel elements such as (a) an adaptive search bound identification
scheme based on the feasibility and convergence status of evaluated solutions
(b) a probabilistic selection method backed by theoretical formulations of
model mean and uncertainties to conduct search in the predicted space to
identify promising solutions (c) an efficient single infill sampling approach
to balance feasibility, convergence and diversity across different stages of
the search and (d) an adaptive switch to unconstrained search based on certain
search conditions. Numerical experiments are conducted on an extensive range of
challenging constrained problems using low evaluation budgets to simulate
ECMOPs. The performance of PSCMOEA is benchmarked against five competitive
state-of-the-art algorithms, to demonstrate its competitive and consistent
performance.
