Solving Interval Multi-objective Optimization Problems Using Evolutionary Algorithms with Lower Limit of Possibility Degree

Interval multi-objective optimization problems (IMOPs) are popular in real-world applications. However, since the optimized objectives not only are multiple but also contain interval parameters, there have been few methods of solving them up to date. We presented a novel method of effectively solving the problems above in this study. In this method, the lower limit of the possibility degree was defined and used to describe a dominance relation of IMOPs. The dominance was further employed to modify the fast non-dominated sorting of Non-dominated sorting genetic algorithm II (NSGA-II). After analyzing its performance, our method was applied to four IMOPs and compared with two typical optimization methods. The experimental results confirmed the advantages of our method.