Innovative application of deep learning and genetic algorithm based on biomechanics in enterprise economics and audit management
Abstract
With the increasing demand for enterprise economic management in complex and dynamic environments, the interdisciplinary application of biomechanics has shown significant potential. This article explores the innovative practices of genetic algorithms and deep learning in optimizing enterprise economic management. Genetic algorithm simulates biological evolution mechanisms such as natural selection and genetic variation to achieve multi-dimensional and multi-level optimization of enterprise economic models, improving decision-making efficiency and adaptability. Deep learning draws on the structural characteristics of biological neural networks to solve problems such as insufficient data and model overfitting, optimizing the intelligence level of resource allocation, performance evaluation, and strategic planning in enterprise management. On this basis, this paper introduces the concept of biomechanics to further improve the adaptability and efficiency of the model. Biomechanics emphasizes the movement and adaptability of organisms in complex environments, which provides a new perspective for corporate economic management. By simulating the dynamic adjustment mechanism of organisms in the face of external pressure, enterprises can respond to market changes more flexibly and optimize resource allocation and decision-making processes. In addition, this article proposes a comprehensive framework that combines multi-level genetic algorithms and deep learning, and verifies its effectiveness in dynamic market environments through case studies. Research has shown that biomechanics not only provides theoretical support for enterprise economic management, but also offers efficient and sustainable pathways for solving complex economic problems. By incorporating the inspiration of biomechanics into the optimization practice of corporate economic management, enterprises can better adapt to market changes, improve the flexibility and efficiency of decision-making, and point the way for future economic management innovation.
References
1. Qiu T, Chi J, Zhou X, et al. Edge computing in industrial internet of things: Architecture, advances and challenges. IEEE Communications Surveys & Tutorials. 2020; 22(4): 2462-2488.
2. Chen C, Liu L, Qiu T, et al. Delay-aware grid-based geographic routing in urban VANETs: A backbone approach. IEEE/ACM Transactions on Networking. 2019; 27(6): 2324-2337.
3. Tang J, Yang Y, Qi Y. A hybrid algorithm for urban transit schedule optimization. Physica A: Statistical Mechanics and its Applications. 2018; 512: 745-755.
4. Cui H, Liu L, Yang Y, Zhu M. A Two-Stage Scheduling Model for the Tunnel Collapse under Construction: Rescue and Reconstruction. Energies. 2022; 15(3): 743.
5. Fathollahi-Fard AM, Hajiaghaei-Keshteli M, Mirjalili S. A set of efficient heuristics for a home healthcare problem. Neural Computing and Applications. 2020; 32(10): 6185-6205.
6. Soto-Mendoza V, García-Calvillo I, Ruiz-y-Ruiz E, Pérez-Terrazas J. A hybrid grasshopper optimization algorithm applied to the open vehicle routing problem. Algorithms. 2020; 13(4); 96.
7. Guo A, Yuan C. Network intelligent control and traffic optimization based on SDN and artificial intelligence. Electronics. 20221; 10(6): 700.
8. Lin K, Li C, Li Y, et al. Distributed learning for vehicle routing decision in software defined Internet of vehicles. IEEE Transactions on Intelligent Transportation Systems. 2020; 22(6): 3730-3741.
9. Hu Y, Dong L, Xu L. Multi-AGV dispatching and routing problem based on a three-stage decomposition method. Mathematical Biosciences and Engineering. 2020; 17(5): 5150-5172.
10. Almasi MH, Oh Y, Sadollah A, et al. Urban transit network optimization under variable demand with single and multi-objective approaches using metaheuristics: The case of Daejeon, Korea. International Journal of Sustainable Transportation. 2021; 15(5): 386-406.
11. Sadeghi, M., Aghayan, I., & Ghaznavi, M. (2020). A cuckoo search based approach to design sustainable transit network. Transportation Letters, 13(9), 635–648. https://doi.org/10.1080/19427867.2020.1750767.
12. Thomas J. Savitsky, Steven A. Schluchter. Some Excluded Minors for the Spindle Surface, Journal of Combinatorial Mathematics and Combinatorial Computing, Volume 119. 217-232. DOI: https://doi.org/10.61091/jcmcc119-22.
13. Zhan ZH, Shi L, Tan KC, Zhang J. A survey on evolutionary computation for complex continuous optimization. Artificial Intelligence Review. 2022; 55(1): 59-110.
14. Liu K, Zhai C, Dong, Y. Optimal restoration schedules of transportation network considering resilience. Structure and Infrastructure Engineering. 2021; 17(8): 1141-1154.
15. Su Y, Yu N, Huang B. Green Road–Rail intermodal routing problem with improved pickup and delivery services integrating truck departure time planning under uncertainty: an interactive fuzzy programming approach. Complex & Intelligent Systems. 2022; 8(2); 1459-1486.
16. Xu Y, Tao Y, Zhang C, et al. Review of digital economy research in China: a framework analysis based on bibliometrics. Computational Intelligence and Neuroscience. 2022; (1): 2427034.
17. Zheng, Y., Xu, Z. & Xiao, A. Deep learning in economics: a systematic and critical review. Artif Intell Rev 56, 9497–9539 (2023). https://doi.org/10.1007/s10462-022-10272-8.
18. Al-Karkhi, M. I., & Rza̧dkowski, G. (2025). Innovative Machine Learning Approaches for Complexity in Economic Forecasting and SME Growth: A Comprehensive Review. Journal of Economy and Technology, https://doi.org/10.1016/j.ject.2025.01.001. ISSN 2949-9488.
19. Bedoui, R., Benkraiem, R., Guesmi, K., & Kedidi, I. (2023). Portfolio optimization through hybrid deep learning and genetic algorithms vine Copula-GARCH-EVT-CvaR model. Technological Forecasting and Social Change, 197, 122887. https://doi.org/10.1016/j.techfore.2023.122887. ISSN 0040-1625.
20. Han, R., Qi, F., Wang, H., & Yi, M. (2024). Innovative machine learning approach for analysing biomechanical factors in running-related injuries. Molecular & Cellular Biomechanics, 21(3), 530. https://doi.org/10.62617/mcb530.
21. Wu, C., Xu, Y., Fang, J. et al. Machine Learning in Biomaterials, Biomechanics/Mechanobiology, and Biofabrication: State of the Art and Perspective. Arch Computat Methods Eng 31, 3699–3765 (2024). https://doi.org/10.1007/s11831-024-10100-y.
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