Research on the Geographical Rationality of FAI Allocation in Guangzhou Districts Based on Differential Evolution

Lewei Huang; Haojie Xiang; Yutian Zhang

doi:10.54097/kvkm7g90

Authors

Lewei Huang
Haojie Xiang
Yutian Zhang

DOI:

https://doi.org/10.54097/kvkm7g90

Keywords:

FAI, Geographical Adjacency, Differential Evolution, Kepler Optimization.

Abstract

The allocation of Fixed Asset Investment (FAI) across districts in Guangzhou significantly influences the coordinated development of the regional economy. Particularly under the constraints of geographical adjacency, the rationality of FAI allocation has garnered considerable attention. In this study, we propose a regional layout optimization method based on soft constraints of geographical adjacency to explore the rationality of FAI allocation. This paper constructs a model integrating economic indicators such as GDP, resident population, total retail sales of consumer goods, and FAI. The weights of these indicators are calculated using Gradient Boosting Decision Tree (GBDT) and Principal Component Analysis (PCA). The rationality of FAI allocation in 2022 is analyzed by combining Multilayer Perceptron (MLP) and Differential Evolution (DE). Furthermore, Kepler Optimization Algorithm (KOA) is introduced to model and optimize the decline in FAI in some districts in 2023, thereby enhancing the analytical accuracy. The results indicate that FAI allocation across Guangzhou districts exhibits a certain level of rationality under geographical adjacency constraints. However, districts such as Panyu and Nansha still require optimized investment strategies. This study provides quantitative support for policymakers, contributing to enhanced regional economic resilience and coordinated development.

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References

[1] Hou S, Zhang Y, Song L. Digital finance and regional economic resilience: evidence from 283 cities in China [J]. Heliyon, 2023, 9(10).

[2] Huo T, Cao R, Xia N, et al. Spatial correlation network structure of China's building carbon emissions and its driving factors: a social network analysis method [J]. Journal of Environmental Management, 2022, 320: 115808.

[3] Liu X, Zhu J, Guo J, et al. Spatial association and explanation of China’s digital financial inclusion development based on the network analysis method [J]. Complexity, 2021, 2021(1): 6649894.

[4] Liu S, Xue L. The spatio-temporal heterogeneity of county-level economic development and primary drivers across the Loess Plateau, China [J]. Journal of Geographical Sciences, 2021, 31(3): 423-436.

[5] Avdagic Z, Smajevic A, Omanovic S, et al. Path route layout design optimization using genetic algorithm: based on control mechanisms for on-line crossover intersection positions and bit targeted mutation [J]. Journal of Ambient Intelligence and Humanized Computing, 2022: 1-13.

[6] Qi X, Khattak S, Zaib A, et al. Energy efficient resource allocation for 5G heterogeneous networks using genetic algorithm [J]. IEEE Access, 2021, 9: 160510-160520.

[7] Yan Y. Genetic Algorithm Based Method for Signal Channel Allocation of Mobile Base Station Optimization [C]//Journal of Physics: Conference Series. IOP Publishing, 2021, 1952(3): 032040.

[8] Yang Y, Liu J, Tan S, et al. A multi-objective differential evolution algorithm based on domination and constraint-handling switching[J]. Information Sciences, 2021, 579: 796-813.

[9] Q. I S, V. I X, K.I. S, et al. Factors affecting regional investment potential [J]. ASIAN JOURNAL OF MULTIDIMENSIONAL RESEARCH, 2021, 10(5): 404-409.

[10] Abdel-Basset M, Mohamed R, Azeem S A A, et al. Kepler optimization algorithm: A new metaheuristic algorithm inspired by Kepler’s laws of planetary motion[J]. Knowledge-based systems, 2023, 268: 110454.