Application of Particle Swarm Optimization Algorithm in Talent Policy System Optimization

As a macro-management system, the complexity of the talent policy system is reflected on that the evaluation results of policy factors are hard to quantify, and the mismatching between the system optimization direction and the social and psychological requirements of talents, et al. In order to solve above problems, a Shaanxi province talents policy system is used as example, a questionnaire about policy satisfaction, engagement and demission tendency is designed and the questionnaire data are collected by using empirical survey method. Based on the questionnaire data, the chaotic particle swarm optimization (CPSO) algorithm is used to build the relationship model for talent policy system, i.e. the mathematical model of the talent policy system. By analyzing the gain coefficient of the model, the contribution rate of different talents policy for the policy satisfaction, engagement and the demission tendency can be obtained. The simulation results show that, compared with the traditional regression approach to build the mathematical model of the talent policy system, the CPSO method has high accuracy, low complexity for computer realization and can be extended to the optimization of other policy systems.