# Multi-objective optimisation of path and space utilisation in landscape garden green space design

## TL;DR

This paper finds that multi-objective evolutionary algorithms optimize garden design better than other methods in urban areas.

## Contribution

The study demonstrates the superiority of MOEAs in optimizing green space design for urban environments.

## Key findings

- MOEAs achieved 90.2% space utilization efficiency, outperforming other algorithms.
- MOEAs provided a shorter path length (140.3 m) and higher aesthetic quality (9.2).
- Urban areas like Tongzhou benefit most from MOEAs in green space optimization.

## Abstract

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.