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A preference multi-objective optimization based on adaptive rank clone and differential evolution

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Abstract

Evolutionary multi-objective optimization (EMO) algorithms have been used in various real-world applications. However, most of the Pareto domination based multi-objective optimization evolutionary algorithms are not suitable for many-objective optimization. Recently, EMO algorithm incorporated decision maker’s preferences became a new trend for solving many-objective problems and showed a good performance. In this paper, we first use a new selection scheme and an adaptive rank based clone scheme to exploit the dynamic information of the online antibody population. Moreover, a special differential evolution (DE) scheme is combined with directional information by selecting parents for the DE calculation according to the ranks of individuals within a population. So the dominated solutions can learn the information of the non-dominated ones by using directional information. The proposed method has been extensively compared with two-archive algorithm, light beam search non-dominated sorting genetic algorithm II and preference rank immune memory clone selection algorithm over several benchmark multi-objective optimization problems with from two to ten objectives. The experimental results indicate that the proposed algorithm achieves competitive results.

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Acknowledgments

The authors would like to thank the editor and the reviewers for helpful comments that greatly improved the paper. This work was supported by the National Natural Science Foundation of China (No. 60803098, No. 61001202, No. 61203303, and No. 61103119), Research Fund for the Doctoral Program of Higher Education of China (No. 20070701022); the Provincial Natural Science Foundation of Shaanxi of China (2010JM8030), and the Fundamental Research Funds for the Central Universities (No. K50511020014, No. K50510020011).

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Authors and Affiliations

  1. Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, 710071, China

    Ruochen Liu, Xiao Wang, Jing Liu, Lingfen Fang & Licheng Jiao

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  1. Ruochen Liu
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  2. Xiao Wang
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  3. Jing Liu
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  4. Lingfen Fang
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  5. Licheng Jiao
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Correspondence to Ruochen Liu.

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Liu, R., Wang, X., Liu, J. et al. A preference multi-objective optimization based on adaptive rank clone and differential evolution. Nat Comput 12, 109–132 (2013). https://doi.org/10.1007/s11047-012-9339-4

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