Uncertainty is an important feature abstracted from real-world applications. Multi-objective optimization problems with uncertainty can always be characterized as robust multi-objective optimization problems. Over recent years, multi-objective optimization evolutionary algorithms have demonstrated the success in solving multi-objective optimization problems. However, most of them do not consider disturbance in the design. In order to handling the uncertainty in the optimization problem, we first
