Multi-objective optimisation of path and space utilisation in landscape garden green space design
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| Udgivet i: | PLoS One vol. 20, no. 7 (Jul 2025), p. e0326374 |
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Public Library of Science
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| Online adgang: | Citation/Abstract Full Text Full Text - PDF |
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| 003 | UK-CbPIL | ||
| 022 | |a 1932-6203 | ||
| 024 | 7 | |a 10.1371/journal.pone.0326374 |2 doi | |
| 035 | |a 3226286342 | ||
| 045 | 2 | |b d20250701 |b d20250731 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Yu, Jia | |
| 245 | 1 | |a Multi-objective optimisation of path and space utilisation in landscape garden green space design | |
| 260 | |b Public Library of Science |c Jul 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Green infrastructure | ||
| 653 | |a Urbanization | ||
| 653 | |a Urban environments | ||
| 653 | |a User experience | ||
| 653 | |a Pheromones | ||
| 653 | |a Population density | ||
| 653 | |a Algorithms | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Mutation | ||
| 653 | |a Landscape architecture | ||
| 653 | |a Efficiency | ||
| 653 | |a Urban areas | ||
| 653 | |a Layouts | ||
| 653 | |a Multiple objective analysis | ||
| 653 | |a Objectives | ||
| 653 | |a Design | ||
| 653 | |a Ant colony optimization | ||
| 653 | |a Foraging behavior | ||
| 653 | |a Pareto optimum | ||
| 653 | |a Aesthetics | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Design optimization | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Gardens & gardening | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Song, Jiazhe | |
| 700 | 1 | |a Lan, Huihui | |
| 700 | 1 | |a Zhang, Yugui | |
| 773 | 0 | |t PLoS One |g vol. 20, no. 7 (Jul 2025), p. e0326374 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3226286342/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3226286342/fulltext/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3226286342/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |