文档介绍：多目标粒子群优化算法在配置城市土地使用上的应用
Considering the ever-increasing urban population, it appears that land management is of major importance. Land uses must be properly arranged so that they do not interfere with one another and can meet each other’s needs as much as possible; this goal is a challenge of urban land-use planning. The main objective of this research is to use Multi-Objective Particle Swarm Optimization algorithm to find the optimum arrangement of urban land uses in parcel level, considering multiple objectives and constraints simultaneously. Geospatial Information System is used to prepare the data and to study different spatial scenarios when developing the model. To optimize the land-use arrangement, four objectives are defined: patibility, maximizing dependency, maximizing suitability, and pactness of land uses. These objectives are characterized based on the requirements of planners. As a result of optimization, the user is provided with a set of optimum land-use arrangements, the Pareto-front solutions. The user can select the most appropriate solutions according to his/her priorities. The method was tested using the data of region 7, district 1 of Tehran. The results showed an acceptable level of repeatability and stability for the optimization algorithm. The model uses parcel instead of urban blocks, as the spatial unit.
Moreover, it considers a variety of land uses and tries to optimize several objectives Simultaneously.
1摘要:考虑到不断增加的城市人口,土地管理看起来就具有重大意义。土地利用必须妥善安排,使它们不会干扰彼此并尽可能满足对方的需要;这个目标对于城市土地利用规划是一个挑战。本研究的主要目的是同时考虑多个目标
限制,利用多目标粒子群优化算法来找到最佳用于城市土地安排地块的水平。地理空间信息系统是在开发模型时,用来准备数据和研究不同空间场景。为了优化土地利用布局,定义四个目标为:最大限度地兼容,最大限度地依赖关系,最大限度地提高适用性,并最大限度地提高土地利用的紧凑性。这些目标的特点是根据规划的要求,帕累托以前的解决方案其结果是向用户提供一组最佳的土地利用安排。用户可以选择最合适的解决方案根据他/她的重点。该方法使用区域7德黑兰1的数据进行了测试。结果表明了是一个重复性和稳定性可接受的优化算法。该模型使用地块而不是城市街区地块作为空间单元。
此外,同时它考虑不同的土地用途并试图优化多个目标
关键词:安排;城市,土地利用,地理信息系统;优化; MOPSO
Land-use optimization is a method of resource allocation, in which different activities or land uses are all