Evaluating spatial design techniques for solving land-use allocation problems.
Evaluating spatial design techniques for solving land-use allocation problems.
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DOI：
10.1080/0964056042000308184
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发表时间：
2005-01-01
影响因子：
3.9
通讯作者：
van Herwijnen, M.
中科院分区：
文献类型：
--
作者：
Aerts, J. C. J. H.;Herwijnen, M. van;van Herwijnen, M.
本研究探讨使用空间优化技术多站点土地利用分配问题（MLUA）。“多地点”是指在一个地区分配一种以上土地使用类型的问题，这是一个困难的问题，因为它们涉及多个利益攸关方，其目标和目的相互冲突。空间优化方法包括（1）优化模型和（2）求解模型的算法。本研究展示了一个目标规划模型来解决MLUA问题。该模型求解采用模拟退火和遗传算法。特别注意的是，在模型中引入了空间紧凑性目标。结果表明，在优化模型中的紧凑性目标产生紧凑的斑块相同的土地利用，使用模拟退火程序和遗传算法。此外，它似乎，使用适当的设置紧凑性目标，土地利用斑块之间的连接性得到促进。该方法被测试用于虚构的研究，然后被证明用于真实的案例研究，两者都测量20*20个细胞。遗传算法在求解时间和实现紧凑性方面通常比模拟退火算法表现得更好。
This study examines the use of spatial optimization techniques for multi-site land-use allocation problems (MLUA). 'Multi-site' refers to the problem of allocating more than one land-use type in an area, which are difficult problems as they involve multiple stakeholders with conflicting goals and objectives. Spatial optimization methods consist of (1) an optimization model and (2) an algorithm to solve the model. This study demonstrates a goal-programming model to solve the MLUA problem. The model is solved using both simulated annealing and genetic algorithms. Special attention has been given to introduce a spatial compactness objective in the model. It is shown that the compactness objectives in the optimization model generate compact patches of the same land use for using both the simulated annealing procedure and the genetic algorithm. In addition, it appears that using the proper settings of the compactness objectives, connectivity between patches of land use is promoted. The method is tested for a fictive study and then demonstrated for a real case study, both measuring 20*20 cells. The genetic algorithm generally performs better than simulated annealing in terms of solution time and achieving compactness.