Journal of Jilin University(Engineering and Technology Edition) ›› 2020, Vol. 50 ›› Issue (5): 1856-1861.doi: 10.13229/j.cnki.jdxbgxb20190923

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Design of fuzzy comprehensive evaluation system for talent quality based on multi⁃objective evolutionary algorithm

Lei JIANG1(),Ren-chu GUAN2   

  1. 1.Office of Talent Work，Jilin University，Changchun 130011, China
    2.College of Computer Science and Technology, Jilin University，Changchun 130011, China
  • Received:2019-09-25 Online:2020-09-01 Published:2020-09-16

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Abstract:

When the evaluation system designed by the current method is used to evaluate the quality of talents, the evaluation results that take a long time have a large error, and there are problems of low evaluation efficiency and low evaluation accuracy. The design method of fuzzy comprehensive evaluation system for talent quality based on multi-objective evolutionary algorithm is proposed. The overall structure of fuzzy comprehensive evaluation system for talent quality is constituted by input module, fuzzy comprehensive evaluation module, report processing module and system maintenance module. AHP is used to calculate the corresponding weights of talent quality evaluation indicators, and a fuzzy comprehensive evaluation model of talent quality is constructed. A multi-objective evolutionary algorithm is used to solve the fuzzy comprehensive evaluation model of talent quality, to achieve the evaluation of talent quality, and to complete the fuzzy comprehensive evaluation of talent quality System design. Experimental results show that the proposed method has high evaluation efficiency and high evaluation accuracy.

Key words: computer application, multi-objective evolutionary algorithm, talent quality, fuzzy comprehensive evaluation, system design

CLC Number: 

  • TP18

Fig.1

Structure of fuzzy comprehensive evaluation system for talent quality"

Fig.2

Block diagram of evaluation procedure"

Table1

Evaluation time of different methods"

迭代次数本文算法证据推理系统设计方法霍尔三维结构模型系统设计方法
10.511.321.23
20.651.021.26
30.711.621.35
40.621.361.32
50.561.261.56
60.531.061.32

Fig.3

CPU usage analysis"

Fig.4

Evaluation system results generation interface"

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