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

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  • 1College of Political Science and Law, Jiangxi Normal University, Jiangxi, Nanchang 330000, China.

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|September 12, 2022
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Summary
This summary is machine-generated.

Optimizing human resource (HR) allocation improves business profitability and productivity. An improved Particle Swarm Optimization (PSO) model enhances HR configuration accuracy by 5%, achieving 94% precision.

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Area of Science:

  • Business Management
  • Operations Research
  • Computational Intelligence

Background:

  • Effective human resource (HR) distribution is crucial for business performance, impacting utilization rates, profitability, and labor productivity.
  • Current methods for HR allocation may lack quantitative precision and operability for complex business environments.

Purpose of the Study:

  • To develop an enterprise HR optimal allocation model using Particle Swarm Optimization (PSO).
  • To provide a quantitative management method for HR configuration optimization.
  • To enhance the accuracy and efficiency of HR allocation strategies.

Main Methods:

  • Introduction of core concepts: Human Resources (HR) and HR allocation.
  • Development of an enterprise HR optimal allocation model based on PSO.
  • Creation of an improved PSO algorithm incorporating system analysis and quantitative evaluation for HR configuration optimization.

Main Results:

  • The improved PSO algorithm demonstrates a quick convergence rate.
  • The enhanced algorithm achieves an average error rate approximately 5% lower than conventional methods.
  • Numerical simulations confirm the algorithm's effectiveness, with an overall accuracy of roughly 94%.

Conclusions:

  • The proposed HR optimal allocation model effectively optimizes HR configuration.
  • The improved PSO algorithm offers a precise and efficient quantitative management approach for HR optimization.
  • This method provides targeted tactics for enhancing HR configuration in businesses.