  Multi-objective optimization problems with constraints (CMOPs) are generally
considered more challenging than those without constraints. This in part can be
attributed to the creation of infeasible regions generated by the constraint
functions, and/or the interaction between constraints and objectives. In this
paper, we explore the relationship between constrained multi-objective
evolutionary algorithms (CMOEAs) performance and CMOP instances characteristics
using Instance Space Analysis (ISA). To do this, we extend recent work focused
on the use of Landscape Analysis features to characterise CMOP. Specifically,
we scrutinise the multi-objective landscape and introduce new features to
describe the multi-objective-violation landscape, formed by the interaction
between constraint violation and multi-objective fitness. Detailed evaluation
of problem-algorithm footprints spanning six CMOP benchmark suites and fifteen
CMOEAs, illustrates that ISA can effectively capture the strength and weakness
of the CMOEAs. We conclude that two key characteristics, the isolation of
non-dominate set and the correlation between constraints and objectives
evolvability, have the greatest impact on algorithm performance. However, the
current benchmarks problems do not provide enough diversity to fully reveal the
efficacy of CMOEAs evaluated.
