SUN Jing, GONG Dunwei. Solving Interval Multi-objective Optimization Problems Using Evolutionary Algorithms with Lower Limit of Possibility Degree[J]. Chinese Journal of Electronics, 2013, 22(2): 269-272.
Citation: SUN Jing, GONG Dunwei. Solving Interval Multi-objective Optimization Problems Using Evolutionary Algorithms with Lower Limit of Possibility Degree[J]. Chinese Journal of Electronics, 2013, 22(2): 269-272.

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

Funds:  This work is supported by the National Natural Science Foundation of China (No.6077044), Program for New Century Excellent Talents in Universities (No.NCET-07-0802), and Natural Science Foundation of HHIT (No.2010150037).
  • Received Date: 2011-11-01
  • Rev Recd Date: 2012-01-01
  • Publish Date: 2013-04-25
  • 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.
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