Research on the Model and Algorithm of Human Resource Optimization Based on Data Driven

Abstract

In order to improve the accuracy of human resource cost evaluation, this paper designs a data-driven human resource cost evaluation algorithm. This paper collects the data of human resource cost evaluation, and uses chaos theory to reconstruct the data to restore the characteristics of human resource cost changes. The human resource cost evaluation algorithm is established by extreme learning machine, and the extreme learning machine is optimized by particle swarm optimization. Finally, the simulation experiment of human resource cost evaluation is carried out. The results show that the proposed algorithm can reflect the changing characteristics of human resource cost. This method improves the evaluation results of human resource cost and obtains better results than other human resource cost evaluation models. This method provides a theoretical reference for the research of data-driven human resource optimal allocation model and algorithm.