  Although synthetic test problems are widely used for the performance
assessment of evolutionary multi-objective optimization algorithms, they are
likely to include unrealistic properties which may lead to
overestimation/underestimation. To address this issue, we present a
multi-objective optimization problem suite consisting of 16 bound-constrained
real-world problems. The problem suite includes various problems in terms of
the number of objectives, the shape of the Pareto front, and the type of design
variables. 4 out of the 16 problems are multi-objective mixed-integer
optimization problems. We provide Java, C, and Matlab source codes of the 16
problems so that they are available in an off-the-shelf manner. We examine an
approximated Pareto front of each test problem. We also analyze the performance
of six representative evolutionary multi-objective optimization algorithms on
the 16 problems. In addition to the 16 problems, we present 8 constrained
multi-objective real-world problems.
