STUDY ON SPATIAL PLANNING OF STREET OUTLETS IN SMART CITIES BASED ON OPTIMISED NSGA-II

With the rapid growth of China's economy and the rapid development of information technology, the concept of using intelligent cities to realise modern urban governance in China has gradually matured. Nowadays, the characteristics of urban spatial structure are diverse, and the internal spatial form of cities is becoming increasingly complex. Currently, world cities are undergoing a profound reshaping of spatial design. In this paper, based on the improved nondominated sorting genetic algorithm (NSGA)-II algorithm, the cumulative ranking adaptation assignment strategy is proposed and introduced along with the arithmetic crossoperator, which significantly improves the convergence speed of the algorithm. Secondly, the theory of complex adaptive systems (CASs) combines computer and urban governance to enhance the selfregulation ability of smart cities to a certain extent and better adapt to external environmental changes. Finally, the kernel density and network analysis of commercial outlets are carried out to determine the distribution pattern and characteristics of each type of business in each region. For the urban construction of Pingdingshan City, regression analysis is used to point out the different situations with other cities from the perspective of quantitative analysis. The results show that the commercial point density is positively correlated with the global integration degree and negatively correlated with the total depth value. In addition, enhancing the accessibility of the main road network and strengthening the construction of rail transportation can effectively strengthen the interaction between urban areas of smart cities as well as improve the layout of urban spatial structure.