  Despite the increasing interest in constrained multiobjective optimization in
recent years, constrained multiobjective optimization problems (CMOPs) are
still unsatisfactory understood and characterized. For this reason, the
selection of appropriate CMOPs for benchmarking is difficult and lacks a formal
background. We address this issue by extending landscape analysis to
constrained multiobjective optimization. By employing four exploratory
landscape analysis techniques, we propose 29 landscape features (of which 19
are novel) to characterize CMOPs. These landscape features are then used to
compare eight frequently used artificial test suites against a recently
proposed suite consisting of real-world problems based on physical models. The
experimental results reveal that the artificial test problems fail to
adequately represent some realistic characteristics, such as strong negative
correlation between the objectives and constraints. Moreover, our findings show
that all the studied artificial test suites have advantages and limitations,
and that no "perfect" suite exists. Benchmark designers can use the obtained
results to select or generate appropriate CMOP instances based on the
characteristics they want to explore.
