【正文】 optimize three objective functions: minimizing traffic, minimizing the costs of transportation, and minimizing current landuse changes. In this initiative algorithm, the optimization process was not performed simultaneously。 instead, it was applied step by step for any of the objective functions, and the best results were then taken for optimization of the next function. LigmannZielinska et al. (2008) focused on the efficient utilization of urban space through infill development,patibility of adjacent land uses, and defensible redevelopment. Cao et al. (2011) used NonDominated Sorting Genetic Algorithm (NSGAII) to propose optimal landuse scenarios with three objective functions: minimizing conversion costs, maximizing accessibility, and maximizing patibilities between land uses.在其他一些研究中，目標是專注于Pareto前沿在多目標模式下同時優(yōu)化。 Pareto解的集合概念中的Deb等適當?shù)拿枋?。?2002）和科埃略科埃略等人。 （2007年） 。帕累托解的集合是德布等的描述。2002）和Coello Coello等人。2007。帕累托集通常是獨立的相對重要的目標，使其適合于復雜的應用，例如土地利用規(guī)劃。土地利用優(yōu)化的許多研究都使用了Pareto前沿。例如，馮和林（ 1999）采用多目標累積遺傳算法（ CGA ）累計產生城市土地不同的場景用來城市規(guī)劃，城市區(qū)域為空間單元。目標函數(shù)是最大化用于開發(fā)的土地的適宜性和最大化相鄰區(qū)的兼容性。Member等（ 2000 ）使用了主動多目標CGA優(yōu)化三個目標函數(shù)：最小化交通，減少運輸成本，減少土地利用現(xiàn)狀的變化。在這一倡議算法，優(yōu)化過程中不同時進行；相反，它是一步一步的任何目標函數(shù)，得到最好的結果用于隨后采取的下一個函數(shù)的優(yōu)化。 Ligmann 杰琳斯卡等。 （ 2008）集中在城市空間的有效利用，通過加密開發(fā)，相鄰土地用途的兼容性，且正當?shù)闹亟ā?Cao等。 （ 2011 ）使用的非支配排序遺傳算法（ NSGAII ）提出了優(yōu)化土地利用三目標函數(shù)最小化的情景：轉換成本，最大化可達性，最大限度地土地使用兼容性。The main objective of this study is to optimize the arrangement of urban land uses in parcel level using MultiObjective PSO (MOPSO) algorithm, considering multiple objectives and constraints simultaneously. In contrast to the abovementioned studies, in this research, the main objectives of landuse arrangement (patibility, dependency, suitability, and pactness) are considered together. In other words, the aim is to optimize the arrangement of urban land uses with respect to all those parameters. This indicates that many objectives have to be considered simultaneously, with a vast search space (many possible arrangements of land uses). The second difference of this research with others is in the usage of PSO for optimization. As indicated in the above literature review, most of the research on multiobjective landuse optimization is based on versions of Genetic Algorithm (GA). The main difference between PSO and GA methods is that PSO does not need genetic operators such as crossover and mutation, which are usually difficult to implement. Moreover, their information sharing mechanism is different: In GA, the information sharing is among all chromosomes, whereas in PSO, only the ‘best’ particle shares its information with others (Parsopoulos and Vrahatis 2010). In general, the main advantage of PSO is the flexibility and simplicity of its operators (Engelbrecht 2006, Van den Bergh and Engelbrecht 2006). The output of the MOPSO is a Pareto front of optimized answers, among which the user can select the most preferable answer based on his/her own priorities. This model proposes several land arrangements to support decisionmaking based on parameters specified by a decisionmaker.本研究的主要目的是在考慮多重目標同時約束下采用多目標粒子群算法（ MOPSO ）用于優(yōu)化城市土地地塊水平線的安排。相反，在上述研究中，土地利用布局的主要目標（相容性，依賴性，適宜性，和壓實度）被認為是在一起的。換句話說，我們的目標是優(yōu)化城市土地利用相對于這些參數(shù)的布置。這表明許多目標必須同時考慮，具有廣闊的搜索空間（多土地用途可能的安排） 。在上述文獻的回顧表明，大多數(shù)對多目標的土地利用優(yōu)化的研究是基于版本的遺傳算法（GA）。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。此外，他們的信息共享機制是不同的：在遺傳算法中，信息共享是所有染色體中，而在PSO中，只有39。最好39。的顆粒與他人分享它的信息（Parsopoulos和Vrahatis2010）。在一般情況下，PSO算法主要的優(yōu)點是其運營的靈活性和簡單性（公司2006，Van den伯格和公司2006）。在MOPSO的輸出是一個帕累托解的集合的優(yōu)化答案，其中，用戶可以選擇基于他/她的自己的優(yōu)先事項的最優(yōu)選的答案。該模型提出了一些基于決策者指定的參數(shù)土地整理決策。2. Fundamentals of the researchIn this section, the concepts of multiobjective optimization and the algorithms applied in this research are discussed.2 該研究的基本原理在本節(jié)中，討論了適用于這項研究的多目標優(yōu)化算法的概念. Multiobjective optimizationThe purpose of multiobjective optimization problems is to simultaneously optimize several objective functions (Hillier and Liberman 1995。 Veldhuizen and Lamont 1999). Thus,there is not only one answer to a problem。 instead, one can obtain a set of answers called the‘Pareto front of the optimized answers’ or the ‘nondominated answers’ (Deb et al. 2002,Coello Coello et al. 2007). If we assume that f 1, f 2, . . . , fm are the objective functions of a problem, then xi can be a nondominated answer if the following conditions are met (Coello Coello and Lomont 2004, Sivanandam and Deepa 2008):? The answer xi should not be worse than xj in all objectives。 in other words,fk (xi) ≥ fk（xj）for all k ∈ {1, 2, . . . ,m} (1)? The answer to xi is better than xj, in at least one objective, that is,fk (xi) fk(xj)for at least one k ∈ {1, 2, . . . ,m} (2)In multiobjective optimization, when the objective functions are plex and/or the search space is extensive, AIbased methods are often used. Using these methods, the entire search space is not investigated. Therefore, there is no guarantee that the definitely optimum solution can be found. Instead, there is a promise that some solutions near enough to the optimum can be found in reasonable time, regardless of the numerous feasible solutions(Coello Coello and Lamont 2004). 多目標優(yōu)化多目標優(yōu)化問題的目的是同時優(yōu)化幾個目標函數(shù)（希利爾和利伯曼1995；該和拉蒙特1999）。因此，還有是不是只有一個答案的問題，反而可以得到一組答案叫“帕累托解的集合的優(yōu)化答案”或“非支配回答，（Deb等人。2002，Coello Coello等人。2007）如果我們假設F 1，F(xiàn) 2。..FM是一個問題的目標函數(shù)，然后xi 可以是一個非支配的答案，如果滿足以下條件，（科埃略科埃略和2004年Lomont，Sivanandam和2008年和Deepa）：?答案xi不應該比所有的目標的xj更糟，換句話說，fk (xi) ≥ fk(xj) for all k ∈ {1, 2, . . . ,m} (1)對于所有的k∈{1，2。 。 。 ，M}（1）?至少在一個目標上答案xi比xj更好，那就是，fk (xi) fk(xj)對于至少一個k∈{1，2。 。 。 ，M}（2）在多目標優(yōu)化的，當目標函數(shù)是復雜的和/或搜索空間是廣泛的，基于AI的方法被經常使用。因此，存在不能保證絕對最佳辦法可以解決。取而代之的是一個承諾，在眾多可行的解決方案中的一些能在合理時間內被發(fā)現(xiàn)近優(yōu)解，（科埃略科埃略和2004年拉蒙特）。. PSO algorithmThe PSO algorithm was developed by Kennedy and Eberhart (1995), as one of the AIbased optimization methods. In PSO, a number of particles are placed in the search space of some problem, each evaluating the objective function (fitness) at its location. In other words, the location of each particle is a solution to the p