  Multiobjective evolutionary algorithms (MOEAs) have been successfully applied
to a number of constrained optimization problems. Many of them adopt mutation
and crossover operators from differential evolution. However, these operators
do not explicitly utilise features of fitness landscapes. To improve the
performance of algorithms, this paper aims at designing a search operator
adapting to fitness landscapes. Through an observation, we find that principle
component analysis (PCA) can be used to characterise fitness landscapes. Based
on this finding, a new search operator, called PCA-projection, is proposed. In
order to verify the effectiveness of PCA-projection, we design two algorithms
enhanced with PCA-projection for solving constrained optimization problems,
called PMODE and HECO-PDE, respectively. Experiments have been conducted on the
IEEE CEC 2017 competition benchmark suite in constrained optimisation. PMODE
and HECO-PDE are compared with the algorithms from the IEEE CEC 2018
competition and another recent MOEA for constrained optimisation. Experimental
results show that an algorithm enhanced with PCA-projection performs better
than its corresponding opponent without this operator. Furthermore, HECO-PDE is
ranked first on all dimensions according to the competition rules. This study
reveals that decomposition-based MOEAs, such as HECO-PDE, are competitive with
best single-objective and multiobjective evolutionary algorithms for
constrained optimisation, but MOEAs based on non-dominance, such as PMODE, may
not.
