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

SUN Jing; GONG Dunwei

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Article Navigation > Chinese Journal of Electronics > 2013 > 22(2): 269-272

SUN Jing and GONG Dunwei, “Solving Interval Multi-objective Optimization Problems Using Evolutionary Algorithms with Lower Limit of Possibility Degree,” Chinese Journal of Electronics, vol. 22, no. 2, pp. 269-272, 2013,

Citation:

SUN Jing and GONG Dunwei, “Solving Interval Multi-objective Optimization Problems Using Evolutionary Algorithms with Lower Limit of Possibility Degree,” Chinese Journal of Electronics, vol. 22, no. 2, pp. 269-272, 2013,

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

SUN Jing^1,2,
GONG Dunwei¹

1.
School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221008, China;
2.
College of Science, Huai Hai Institute of Technology, Lianyungang 221005, China

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

Abstract

Abstract

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.
- Evolutionary algorithm,
- Multi-objective optimization,
- Interval,
- Lower limit of possibility degree

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References(12)

References

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