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1、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 others needs as much as possible; this goal is a challenge of urban la
2、nd-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 th
3、e data and to study different spatial scenarios when developing the model. To optimize the land-use arrangement, four objectives are defined: maximizing compatibility, maximizing dependency, maximizing suitability, and maximizing compactness of land uses. These objectives are characterized based on
4、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
5、 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
6、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
7、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; MOPSOLand-use optimization is a method of resource allocation, in which different activities or land uses are allocated to specific units of land area. These kinds of problems need multiple and often
8、conflicting objectives (such as ecological and economic objectives) to be considered simultaneously (Chandramouli et al. 2009, Xiaoli et al. 2009, Cao et al. 2011, Shifa et al. 2011). Therefore, land-use allocation can be considered as an optimization problem. In multi-objective optimization of land
9、 use (MOLU) model, combinations of different objectives are considered. The commonly used objectives include the improvements related to compatibility and dependency among neighbouring land uses, the suitability of land units for land uses, land-use compactness, and the per capita demand for land us
10、e. These parameters have been studied and discussed by Berke et al. (2006), Talei et al. (2007), Jiang-Ping and Qun (2009), Haque and Asami (2011), and Koomen et al. (2011).土地利用优化是不同的土地使用行为分配其特定的单位土地面积资源配置的一种方法，。这类问题需要考虑多且被认为是同时相互冲突的目标（如生态和经济目标）（chandramouli等人。2009，小李等人。2009，曹等人。2011，发等人。2011）因此，土地利
11、用配置可以被视为一个优化问题。在土地利用多目标优化（陌路）模型时，考虑了不同的组合目标。常用的目标包括改进相关的邻近土地的使用相容性和依赖性，土单位土地利用的适宜性土地利用结构紧凑，和土地利用人均需求。伯克等人对这些参数进行了研究和讨论。 （2006），Talei等。 （2007年），江平与群（2009），哈克和麻美（2011），以及库门等。 （2011年）。Handling many objectives together is usually more complex than handling a single objective. Therefore, many methods are
12、 developed to convert multiple objectives into a single objective. To search the solution space in a single-objective mode, some researchers have used classic methods of optimization such as linear programming (LP). For instance, Maoh and Kanaroglou (2009) used LP to optimize land uses, concentratin
13、g on the relation between land use and traffic. Some other models are based on artificial intelligence (AI) methods. For example, Shiffa et al. (2011) used particle swarm optimization (PSO) to optimize the allocation of land uses, considering maximum suitability of land and a minimum cost of changin
14、g the land shape. In another study by Semboloni (2004), simulated annealing (SA) method was used to optimize the facilities required for residential and commercial areas. The main problem of these methods is that the results depend strongly on the weights given to the objectives or the function used
15、 to combine the objectives into one. Moreover, non-convex optimal solutions cannot be obtained by minimizing linear combinations of objectives (Cao et al. 2011). Besides, decision-makers prefer to explore a set of alternative solutions and their trade-offs regarding different objectives and to make
16、decisions accordingly. To find multiple solutions using such methods, the algorithm has to be run many times, hopefully finding a different solution at each run to create trade-off solutions (Deb et al. 2002).处理许多共同的目标通常比处理一个目标更复杂。因此，许多方法的开发，以多重目标转换成单一目标。在一个单一的目标模式搜索解空间，一些研究人员采用经典的优化方法如线性规划（LP）。例如，例如，他和kanaroglou（2009）使用LP优化土地利用，集中在土地利用与交通之间的关系。其他一些模型是基于人工智能（AI ）方法。例如， Shiffa等。 （ 2011）采用粒子群优化算法（PSO）优化划拨土地使用，考虑最大土地适宜性和最小改变土地形状的成本。在另一项由Semboloni （ 2004）的研究 中，模拟退火（SA）方法被用来优
