Interval multi-objective optimization prob- lems (IMOPs) are popular in real-world applications. How- ever, since the optimized objectives not only are multiple but also contain interval parameters, there have been fe...
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Interval multi-objective optimization prob- lems (IMOPs) are popular in real-world applications. How- ever, 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 de- gree 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 perfor- mance, our method was applied to four IMOPs and com- pared with two typical optimization methods. The exper- imental results confirmed the advantages of our method.
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