路径和空间利用在景观花园中的多目标优化 绿色空间设计
Jia Yu1, Jiazhe Song1, Huihui Lan1
1College of Life Science and Engineering, Shenyang University, Shenyang, China.
PloS one
|July 1, 2025
概括
多目标进化算法 (MOEA) 优化景观花园绿色空间设计,平衡空间,路径和美学效率. MOEA的性能优于其他算法,为城市绿色空间提供了卓越的解决方案.
科学领域:
- 环境科学 环境科学
- 城市规划 城市规划
- 计算智能是一种计算智能.
背景情况:
- 快速的城市化对高效的景观花园设计提出了挑战.
- 优化绿色空间需要平衡多个,往往相互竞争的目标.
研究的目的:
- 为了解决景观花园绿地空间设计中的多目标优化问题.
- 为了比较各种多目标优化算法 (MOEA) 与遗传算法 (GA),粒子群优化 (PSO),群优化 (ACO) 和模拟化 (SA) 等传统方法的性能.
主要方法:
- 应用多个多目标优化算法在通州区的一个案例研究.
- 基于空间利用效率,路径效率和美学质量的评估.
主要成果:
- 与GA,PSO,ACO和SA相比,MOEA在所有三个目标上都表现出优异的表现.
- MOEAs实现了90.2%的空间利用率,140.3米的路径长度,美学评分为9.2.2.
- 这些结果明显超过了GA,PSO,ACO和SA获得的结果.
结论:
- MOEA为优化景观花园绿地设计提供了一种卓越的方法.
- 这些发现强调了MOEA在平衡空间效率,路径优化和美学质量的有效性.
- 这种优化策略对于密集的城市环境尤其有利.
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