Research Article
Quality Evaluation of Human Resource Management Information System Based on Intelligent Optimization Algorithm
@INPROCEEDINGS{10.1007/978-3-030-94182-6_6, author={Bo Sun and Hao-nan Chu}, title={Quality Evaluation of Human Resource Management Information System Based on Intelligent Optimization Algorithm}, proceedings={IoT and Big Data Technologies for Health Care. Second EAI International Conference, IoTCare 2021, Virtual Event, October 18-19, 2021, Proceedings, Part II}, proceedings_a={IOTCARE PART 2}, year={2022}, month={6}, keywords={Intelligent optimization Human resource Information system Quality evaluation}, doi={10.1007/978-3-030-94182-6_6} }- Bo Sun
Hao-nan Chu
Year: 2022
Quality Evaluation of Human Resource Management Information System Based on Intelligent Optimization Algorithm
IOTCARE PART 2
Springer
DOI: 10.1007/978-3-030-94182-6_6
Abstract
The conventional quality evaluation method of human resource management information system can not early warn the deterioration index with small weight, so a quality evaluation method of human resource management information system based on intelligent optimization algorithm is designed. Firstly, the experts’ opinions are widely solicited, information is exchanged repeatedly, and the evaluation index system is established by determining the index type and the scale of the index set, and the weight of the index is assigned by using the quantitative index screening method; the McCall model is used as the quality evaluation model to accurately and quantitatively evaluate the software system index, and finally the ant colony algorithm is selected for the quality intelligent optimization evaluation So far, the research on the quality evaluation method of human resource information system based on intelligent optimization algorithm is completed. The simulation results show that the evaluation results of the method based on intelligent optimization algorithm are consistent with the data collection results, which can accurately reflect the real operation state of the system, and verify the effectiveness of the evaluation model.
