Green space design dataset overview.

This study addresses the multi-objective optimization problem in landscape garden green space design, focusing on optimizing space utilization efficiency, path efficiency, and aesthetic quality. We compare various multi-objective optimization algorithms to solve the problem We compare various multi-objective optimization algorithms to solve the problem, applied to the urban environment of Tongzhou District, which is characterized by rapid urbanization and high population density. Experimental results demonstrate that MOEAs outperforms other optimization algorithms such as GA, PSO, ACO, and SA in all three objectives. Specifically, MOEAs achieved a space utilization efficiency of 90.2%, a path length of 140.3 m, and an aesthetic quality score of 9.2, surpassing the best results from GA (85.3%, 150.2 m, 8.4), PSO (88.5%, 148.6 m, 8.6), ACO (82.4%, 160.5 m, 7.9), and SA (80.1%, 162.4 m, 7.5). In conclusion, MOEAs provides a superior solution for optimizing landscape garden green space design, offering the best balance between spatial efficiency, path optimization, and aesthetic quality, particularly for urban areas like Tongzhou.