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A multi-objective optimization evolutionary algorithm incorporating preference information based on fuzzy logic

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Abstract

A multi-objective optimization evolutionary algorithm incorporating preference information interactively is proposed. A new nine grade evaluation method is used to quantify the linguistic preferences expressed by the decision maker (DM) so as to reduce his/her cognitive overload. When comparing individuals, the classical Pareto dominance relation is commonly used, but it has difficulty in dealing with problems involving large numbers of objectives in which it gives an unmanageable and large set of Pareto optimal solutions. In order to overcome this limitation, a new outranking relation called “strength superior” which is based on the preference information is constructed via a fuzzy inference system to help the algorithm find a few solutions located in the preferred regions, and the graphical user interface is used to realize the interaction between the DM and the algorithm. The computational complexity of the proposed algorithm is analyzed theoretically, and its ability to handle preference information is validated through simulation. The influence of parameters on the performance of the algorithm is discussed and comparisons to another preference guided multi-objective evolutionary algorithm indicate that the proposed algorithm is effective in solving high dimensional optimization problems.

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Authors and Affiliations

  1. School of Automation, Nanjing University of Science and Technology, Nanjing, 210094, China

    Xiaoning Shen, Yu Guo, Qingwei Chen & Weili Hu

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  1. Xiaoning Shen
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  2. Yu Guo
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  3. Qingwei Chen
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  4. Weili Hu
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Correspondence to Xiaoning Shen.

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Supported by National Key Laboratory of Spatial Intelligent Control.

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Shen, X., Guo, Y., Chen, Q. et al. A multi-objective optimization evolutionary algorithm incorporating preference information based on fuzzy logic. Comput Optim Appl 46, 159–188 (2010). https://doi.org/10.1007/s10589-008-9189-2

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