【正文】 ated by high patibilities with some other neighbours. To overe this problem, the objective function can be altered to maximize the minimum amount of patibility as well. Following this objective, each land would have an adequate level of patibility with all its neighbours. Thus, the first objective function may be defined by:在圖3中，是最大距離和最小距離的影響示意圖。Figure 2. Effect of the distance on the patibility objective function. shows the minimumdistance between two land uses and which two landuse types has the maximum effect on each other in this distance (as shown in y axis it is consider 1 in this research) and the shows the maximum radius of effect between two land uses. β shows the distance effect on patibility.圖2。兩個(gè)地塊的的兼容性（記為Compij）i和j與土地利用類型ci 和cj，一個(gè)彼此間的距離dij被定義為：where Compcicj is the patibility of two landuse types Ci and Cj in the patibility matrix.More specifically, the patibility function can be defined by: in which, the function defines the effect of distance and is calculated by Equation (9). For simplicity, in this study, we assume β=1. The general shape of this function is shown in Figure 2.在pcicj是兩種土地利用類型的Ci和Cj的相容性矩陣。 AHP是一個(gè)廣泛應(yīng)用的多準(zhǔn)則評(píng)價(jià)方法（鮑文1993）。得到一個(gè)相對(duì)詳細(xì)的土地使用兼容性評(píng)分和近似類型，它被用來(lái)建立一個(gè)詳細(xì)的兼容性矩陣。本節(jié)的活動(dòng)是基于一個(gè)兩輪德?tīng)柗普{(diào)查。在這種研究， Delphi法被用作于構(gòu)建詳細(xì)兼容性矩陣的框架。這些目標(biāo)函數(shù)將在以下章節(jié)中討論。每一步，單個(gè)粒子所經(jīng)過(guò)的最佳位置(Pbest)和粒子群的最佳位置（ Gbest）被發(fā)現(xiàn)并存儲(chǔ)。該算法查找一個(gè)粒子的位置（該結(jié)構(gòu)土地用途）最好的滿足目標(biāo)函數(shù)。在多目標(biāo)優(yōu)化下，通常情況下準(zhǔn)則一定會(huì)達(dá)到迭代的指定數(shù)量。事實(shí)上，其目的是用較少的粒子選擇一個(gè)超立方體去優(yōu)化的帕累托解的集合的密度。事實(shí)上，其目的是選擇用較少的粒子優(yōu)化的帕累托密度超立方體前。在兩個(gè)目標(biāo)的二維搜索空間中產(chǎn)生的超立方體的例子功能。在圖1中，一個(gè)二維搜索空間和其分裂成超立方體被顯示。在這篇文章中，使用由科埃略科埃略和拉蒙特（ 2004）提出的方法 因?yàn)樗哂懈〉挠?jì)算復(fù)雜度和更快的收斂（雷耶斯 Sierra和科埃略科埃略2006） 。這是第二類別的主要挑戰(zhàn)。在這些方法中，每個(gè)粒子每次只有一個(gè)目標(biāo)函數(shù)進(jìn)行評(píng)價(jià)，最好的位置是按照單一目標(biāo)標(biāo)準(zhǔn)確定PSO規(guī)則，使用相應(yīng)的目標(biāo)函數(shù)。c1和c2值較小時(shí)允許每個(gè)粒子探索地點(diǎn)遠(yuǎn)離已經(jīng)發(fā)現(xiàn)的好點(diǎn)，這些參數(shù)的值越高鼓勵(lì)粒子搜索靠攏前期點(diǎn)比較密集的區(qū)域（二零零六年克萊爾奇）。如果搜索空間的維數(shù)為d，在時(shí)間t的粒子的當(dāng)前位置和速度是向量X，V表示。換句話說(shuō)，每個(gè)粒子的位置是一個(gè)解決問(wèn)題的辦法，它可以被目標(biāo)函數(shù)評(píng)估。 and are the new velocity and location of the particle in the same dimension, respectively。 ，M}（2）在多目標(biāo)優(yōu)化的，當(dāng)目標(biāo)函數(shù)是復(fù)雜的和/或搜索空間是廣泛的，基于AI的方法被經(jīng)常使用。 。2002，Coello Coello等人。 Veldhuizen and Lamont 1999). Thus,there is not only one answer to a problem。在一般情況下，PSO算法主要的優(yōu)點(diǎn)是其運(yùn)營(yíng)的靈活性和簡(jiǎn)單性（公司2006，Van den伯格和公司2006）。PSO和GA方法的主要區(qū)別是， 假如不需要遺傳操作如交叉和變異，PSO通常很難完成。相反，在上述研究中，土地利用布局的主要目標(biāo)（相容性，依賴性，適宜性，和壓實(shí)度）被認(rèn)為是在一起的。 （ 2008）集中在城市空間的有效利用，通過(guò)加密開(kāi)發(fā)，相鄰?fù)恋赜猛镜募嫒菪裕艺?dāng)?shù)闹亟?。目?biāo)函數(shù)是最大化用于開(kāi)發(fā)的土地的適宜性和最大化相鄰區(qū)的兼容性。2007。（ 2002）和科埃略科埃略等人。 2002年）。這些方法的主要問(wèn)題是，結(jié)果強(qiáng)烈地依賴于考慮到目標(biāo)或功能用于結(jié)合成一個(gè)目標(biāo)的權(quán)重。其他一些模型是基于人工智能（AI ）方法。Handling many objectives together is usually more plex than handling a single objective. Therefore, many methods are developed to convert multiple objectives into a single objective. To search the solution space in a singleobjective 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, concentrating 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 changing the land shape. In another study by Semboloni (2004), simulated annealing (SA) method was used to optimize the facilities required for residential and mercial areas. The main problem of these methods is that the results depend strongly on the weights given to the objectives or the function used to bine the objectives into one. Moreover, nonconvex optimal solutions cannot be obtained by minimizing linear binations of objectives (Cao et al. 2011). Besides, decisionmakers prefer to explore a set of alternative solutions and their tradeoffs regarding different objectives and to make 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 tradeoff solutions (Deb et al. 2002).處理許多共同的目標(biāo)通常比處理一個(gè)目標(biāo)更復(fù)雜。伯克等人對(duì)這些參數(shù)進(jìn)行了研究和討論。2011，發(fā)等人。 MOPSOLanduse 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 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, landuse allocation can be considered as an optimization problem. In multiobjective optimization of land use (MOLU) model, binations of different objectives are considered. The monly used objectives include the improvements related to patibility and dependency among neighbouring land uses, the suitability of land units for land uses, landuse pactness, and the per capita demand for land use. These parameters have been studied and discussed by Berke et al. (2006), Talei et al. (2007), JiangPing and Qun (2009), Haque and Asami (2011), and Koomen et al. (2011).土地利用優(yōu)化是不同的土地使用行為分配其特定的單位土地面積資源配置的一種方法。該模型使用地塊而不是城市街區(qū)地塊作為空間單元。這些目標(biāo)的特點(diǎn)是根據(jù)規(guī)劃的要求，帕累托以前的解決方案其結(jié)果是向用戶提供一組最佳的土地利用安排。土地利用必須妥善安排，使它們不會(huì)干擾彼此并盡可能滿足對(duì)方的需要；這個(gè)目標(biāo)對(duì)于城市土地利用規(guī)劃是一個(gè)挑戰(zhàn)。 this goal is a challenge of urban landuse planning. The main objective of this research is to use MultiObjective 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 landuse arrangement, four objectives are defined: maximizing patibility, maximizing dependency, maximizing suitability, and maximizing 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 landuse arrangements, the Paretofront solutions. The user can select the most appropriate solutions according to his/her priorities. The method was tested using the data of region