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Evolutionary algorithms with user’s preferences for solving hybrid interval multi-objective optimization problems

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Abstract

Hybrid interval multi-objective optimization problems are common in real-world applications. These problems involve both explicit and implicit objectives, and the values of these objectives are intervals. Few previous methods are suitable for them. An evolutionary algorithm with a large population and a user’s interval preferences was presented to effectively solve the problems in this paper. In the proposed algorithm, a similarity-based strategy was employed to estimate the interval values of implicit objectives of evolutionary individuals that the user had not evaluated in order to alleviate user fatigue; the user’s preferences to different objectives were expressed precisely as intervals by solving an auxiliary optimization problem; a sorting scheme based on the user’s preferences was proposed to guide the population evolving toward the user’s preferred regions. We applied the proposed method to an interior layout problem, which is a typical optimization problem with both interval parameters in the explicit objective and interval value of the implicit objective. The proposed algorithm was compared with four other optimization algorithms on the interior layout problem. Experimental results validated its effectiveness and superiority over the compared algorithms in terms of solution quality and the number of user’s evaluations.

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Acknowledgments

This work was jointly supported by National Natural Science Foundation of China with grant No. 61375067 and 61403155, National Basic Research Program of China (973 Program) with grant No. 2014CB046306-2, and Natural Science Foundation of Jiangsu Province with grant No. BK2012566. Thanks to Dr. Jianyong Sun for linguistic advice.

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Authors and Affiliations

  1. School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, China

    Dunwei Gong & Yiping Liu

  2. School of Electrical Engineering and Information Engineering, LanZhou University of Technology, Lanzhou, China

    Dunwei Gong

  3. Department of Mechanical, Power and Information Engineering, China University of Mining and Technology, Yinchuan College, Yinchuan, China

    Xinfang Ji

  4. School of Science, Huai Hai Institute of Technology, Lianyungang, China

    Jing Sun

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  1. Dunwei Gong
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  2. Yiping Liu
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  4. Jing Sun
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Correspondence to Xinfang Ji.

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Gong, D., Liu, Y., Ji, X. et al. Evolutionary algorithms with user’s preferences for solving hybrid interval multi-objective optimization problems. Appl Intell 43, 676–694 (2015). https://doi.org/10.1007/s10489-015-0658-x

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