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Designing Landscape of Urban Gardening Based on Optimized Artificial Intelligence Model

The landscape is driven by innovative design, adhering to the concept of “poetic residence, inheritance, and innovation,” which has long served China’s urban and rural development and the construction of ecological civilization and provided high-quality planning and design services for governments at all levels throughout the country; we construct a particle swarm optimization (PSO) landscape pattern spatial optimization model and solution algorithm to optimize the spatial layout of the landscape for economic development, ecological protection, and integrated scenarios in a city in southwest China. The results show that the PSO-based landscape pattern spatial optimization model and algorithm can use particle position to simulate landscape distribution for spatial pattern optimization. In the development of landscape pattern optimization methods, the landscape pattern optimization model with landscape simulation evolution as the core has shown its advantages. In the target year, the dominant landscape of economic development scenario is urban and orchards, and the landscape pattern shows the distribution characteristics of urban, farmland in the western dam area, and orchards in the eastern mountainous area; the dominant landscape of ecological protection scenario is forest, urban, and rural residential and industrial mining; and the landscape pattern shows the distribution characteristics of urban and rural residential and industrial mining, orchards, farmland in the western dam area, and forest in the eastern mountainous area. The landscape pattern shows that the western dam area is dominated by urban and rural residential and industrial, while the eastern mountainous area is dominated by forests and orchards. The integrated scenario has the highest potential for the future, and its economic, ecological, and comprehensive benefits can be optimized, which is the best spatial layout of the landscape pattern in the study area in the target year.