Enterprise Human Resource Optimization Algorithm Using PSO Model in Big Data and Complex Environment

• Published in: Journal of environmental and public health Vol. 2022; no. 1; p. 1244660
• Main Authors: Wang, Xiang, Zhang, Ying
• Format: Journal Article
• Language: English
• Published: New York Hindawi 2022; John Wiley & Sons, Inc
• Subjects: Algorithms; Big data; Competition; Configuration management; Economics; Efficiency; Genetic algorithms; Human resource management; Human resources; Labor economics; Labor productivity; Mathematical optimization; Methods; Numerical analysis; Optimization algorithms; Optimization models; Productivity; Profitability; Quantitative analysis; Structural analysis; Systems analysis; Tactics
• ISSN: 1687-9805; 1687-9813; 1687-9813
• DOI: 10.1155/2022/1244660

The distribution of human resources has a direct impact on the HR utilization rate in businesses, which in turn has an impact on the profitability and labor productivity of those businesses. As a result, this article develops an enterprise HR optimal allocation model based on PSO. The concepts of HR and HR allocation are introduced, and a programme for implementing optimal HR allocation in businesses is provided from the perspectives of scale prediction, structure analysis, and implementation. An HR configuration optimization model is established, providing a specific method of quantitative management for HR configuration optimization, at the same time starting from operability, based on the methods of system analysis and quantitative evaluation, and an improved PSO is created to address this issue. Results from numerical simulations demonstrate this algorithm’s effectiveness. According to the experimental findings, the improved PSO has a quick convergence rate and a roughly 5% lower average error rate than the conventional algorithm. Moreover, this algorithm’s accuracy is roughly 94%. This method offers some targeted tactics for optimizing HR configuration.