<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper constructs a model of the particle swarm algorithm, compares and analyzes the performance of the particle swarm algorithm under the two parameters of <italic>w</italic> and <italic>k</italic> in detail, and solves the constrained optimization problem by the particle swarm algorithm. On the basis of the local optimal value to find the global optimal value, the particle swarm algorithm is improved with reference to the particle’s motion state and behavior. Based on the particle swarm algorithm, a digital enterprise management system is constructed to plan enterprise management operations and optimize efficiency. Finally, we compare the performance of different algorithms in enterprise management risk prediction, analyze the correlation between the management system and enterprise management efficiency, and compare the management efficiency of different enterprises to explore the effect of the particle swarm algorithm in digital enterprise management. The results show that the predictive classification effect of the particle swarm algorithm model reaches more than 95% correct rate, and the management system of the particle swarm algorithm presents significance at 1% and 5% significance level for enterprise management efficiency, respectively.</p>
</abstract>

This paper constructs a model of the particle swarm algorithm, compares and analyzes the performance of the particle swarm algorithm under the two parameters of w and k in detail, and solves the constrained optimization problem by the particle swarm algorithm. On the basis of the local optimal value to find the global optimal value, the particle swarm algorithm is improved with reference to the particle’s motion state and behavior. Based on the particle swarm algorithm, a digital enterprise management system is constructed to plan enterprise management operations and optimize efficiency. Finally, we compare the performance of different algorithms in enterprise management risk prediction, analyze the correlation between the management system and enterprise management efficiency, and compare the management efficiency of different enterprises to explore the effect of the particle swarm algorithm in digital enterprise management. The results show that the predictive classification effect of the particle swarm algorithm model reaches more than 95% correct rate, and the management system of the particle swarm algorithm presents significance at 1% and 5% significance level for enterprise management efficiency, respectively.

Research on the Innovative Application of Particle Swarm Algorithm in the Improvement of Management Efficiency of Digital Enterprises

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper constructs a model of the particle swarm algorithm, compares and analyzes the performance of the particle swarm algorithm under the two parameters...

Research on the Innovative Application of Particle Swarm Algorithm in the Improvement of Management Efficiency of Digital Enterprises

Online veröffentlicht: 05. Dez. 2023

Seitenbereich: -

Eingereicht: 28. Feb. 2023

Akzeptiert: 15. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01368

Schlüsselwörter
Particle swarm algorithm, constrained optimization, Shrinkage factor, Risk prediction, Digital management

© 2023 Xiong Yin, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the Innovative Application of Particle Swarm Algorithm in the Improvement of Management Efficiency of Digital Enterprises

Online veröffentlicht: 05. Dez. 2023

Seitenbereich: -

Eingereicht: 28. Feb. 2023

Akzeptiert: 15. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01368

SchlüsselwörterParticle swarm algorithm, constrained optimization, Shrinkage factor, Risk prediction, Digital management

© 2023 Xiong Yin, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Particle swarm algorithm, constrained optimization, Shrinkage factor, Risk prediction, Digital management