Research on the Innovative Application of Particle Swarm Algorithm in the Improvement of Management Efficiency of Digital Enterprises
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...
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| Format: | Article |
| Language: | English |
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Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
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| Online Access: | https://doi.org/10.2478/amns.2023.2.01368 |
| _version_ | 1797340545140391936 |
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| author | Yin Xiong |
| author_facet | Yin Xiong |
| author_sort | Yin Xiong |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T10:04:37Z |
| format | Article |
| id | doaj.art-56502a07c3e041d59c13c1e1611d5037 |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-08T10:04:37Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-56502a07c3e041d59c13c1e1611d50372024-01-29T08:52:42ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns.2023.2.01368Research on the Innovative Application of Particle Swarm Algorithm in the Improvement of Management Efficiency of Digital EnterprisesYin Xiong01Geely University of China, Chengdu, Sichuan, 610000, China.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.https://doi.org/10.2478/amns.2023.2.01368particle swarm algorithmconstrained optimizationshrinkage factorrisk predictiondigital management90b50 |
| spellingShingle | Yin Xiong Research on the Innovative Application of Particle Swarm Algorithm in the Improvement of Management Efficiency of Digital Enterprises Applied Mathematics and Nonlinear Sciences particle swarm algorithm constrained optimization shrinkage factor risk prediction digital management 90b50 |
| title | Research on the Innovative Application of Particle Swarm Algorithm in the Improvement of Management Efficiency of Digital Enterprises |
| title_full | Research on the Innovative Application of Particle Swarm Algorithm in the Improvement of Management Efficiency of Digital Enterprises |
| title_fullStr | Research on the Innovative Application of Particle Swarm Algorithm in the Improvement of Management Efficiency of Digital Enterprises |
| title_full_unstemmed | Research on the Innovative Application of Particle Swarm Algorithm in the Improvement of Management Efficiency of Digital Enterprises |
| title_short | Research on the Innovative Application of Particle Swarm Algorithm in the Improvement of Management Efficiency of Digital Enterprises |
| title_sort | research on the innovative application of particle swarm algorithm in the improvement of management efficiency of digital enterprises |
| topic | particle swarm algorithm constrained optimization shrinkage factor risk prediction digital management 90b50 |
| url | https://doi.org/10.2478/amns.2023.2.01368 |
| work_keys_str_mv | AT yinxiong researchontheinnovativeapplicationofparticleswarmalgorithmintheimprovementofmanagementefficiencyofdigitalenterprises |