Research on Fast Performance Evaluation Algorithm for Employees Based on Particle Swarm Optimization Algorithm

Abstract

In the contemporary business environment, effective employee performance evaluation plays a critical role in enhancing organizational productivity and employee satisfaction. Traditional evaluation methods often face challenges in terms of accuracy, timeliness, and adaptability to dynamic work conditions. This paper proposes a novel approach for employee performance evaluation using a Particle Swarm Optimization (PSO) algorithm. The aim of this paper is to develop a fast and efficient evaluation system capable of processing large datasets and providing real-time assessments. The proposed algorithm optimizes the evaluation criteria through a swarm of particles, iteratively improving the solution based on pre-defined fitness functions, which capture key performance indicators such as productivity, teamwork, and innovation. Experimental results demonstrate that the PSO-based algorithm significantly outperforms traditional methods in terms of processing speed and evaluation accuracy. Moreover, the system is scalable and adaptable to various organizational structures and industries, offering a robust tool for human resource management. The findings of this research suggest that the integration of PSO into employee performance evaluation systems can lead to more reliable and rapid assessments, ultimately contributing to better decision-making and enhanced organizational performance.