Applied Mathematics and Nonlinear Sciences
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Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 9, Issue 1


Published
on

February 26, 2024


Pages


DOI

Article

Regional economic dispatch analysis and industry chain optimization based on proximal strategy optimization algorithm


Authors

Yichuan Zhang Affiliation:
School of Economics, Guizhou University, Guiyang, Guizhou, 550025, China.


Abstract

In this paper, the traditional proximal strategy optimization algorithm is improved based on traction technology to address the poor efficiency of the conventional proximal strategy optimization algorithm in analyzing regional economic scheduling and industrial chain optimization. The focus is on finding the optimal solution by applying this algorithm in the spatio-temporal coupling characteristics of new infrastructure and regional economic dispatch, the simulation of changes in regional industrial structure and the allocation of industrial chain. The experimental analysis not only verifies the effectiveness of the improved algorithm, but also shows that the level of new infrastructure development shows a specific positive correlation with the degree of development of regional economic dispatch, with a growth rate of 29.26% in 2021. Under the influence of different industrial scenarios and policies, there are significant differences in the path and speed of industrial structure change FPPO-QP algorithm is also above 44Mdps in V2I capacity performance under the interference of 3.5×1060Bytes. In contrast, the performance of the FPPO-LP algorithm has declined. Therefore, the improved algorithm can effectively apply to regional economic dispatch analysis and industrial chain optimization.


Keywords

Proximal strategy optimization, Regional economy, Industrial chain, FPPO-QP algorithm traction technique, 93C62


Citation

Zhang, Y. (2024). Regional economic dispatch analysis and industry chain optimization based on proximal strategy optimization algorithm. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns-2024-0462
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