多目標粒子群優化算法在配置城市土地使用上的應用(已修改)

　

【正文】 多目標粒子群優化算法在配置城市土地使用上的應用Considering the everincreasing 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 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 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：考慮到不斷增加的城市人口，土地管理看起來就具有重大意義。土地利用必須妥善安排，使它們不會干擾彼此并盡可能滿足對方的需要；這個目標對于城市土地利用規劃是一個挑戰。本研究的主要目的是同時考慮多個目標限制，利用多目標粒子群優化算法來找到最佳用于城市土地安排地塊的水平。地理空間信息系統是在開發模型時，用來準備數據和研究不同空間場景。為了優化土地利用布局，定義四個目標為：最大限度地兼容，最大限度地依賴關系，最大限度地提高適用性，并最大限度地提高土地利用的緊湊性。這些目標的特點是根據規劃的要求，帕累托以前的解決方案其結果是向用戶提供一組最佳的土地利用安排。用戶可以選擇最合適的解決方案根據他/她的重點。該方法使用區域7德黑蘭1的數據進行了測試。結果表明了是一個重復性和穩定性可接受的優化算法。該模型使用地塊而不是城市街區地塊作為空間單元。此外，同時它考慮不同的土地用途并試圖優化多個目標關鍵詞：安排。城市，土地利用，地理信息系統。優化。 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。這類問題需要考慮多且被認為是同時相互沖突的目標（如生態和經濟目標）（chandramouli等人。2009，小李等人。2009，曹等人。2011，發等人。2011）因此，土地利用配置可以被視為一個優化問題。在土地利用多目標優化（陌路）模型時，考慮了不同的組合目標。常用的目標包括改進相關的鄰近土地的使用相容性和依賴性，土單位土地利用的適宜性土地利用結構緊湊，和土地利用人均需求。伯克等人對這些參數進行了研究和討論。 （2006），Talei等。 （2007年），江平與群（2009），哈克和麻美（2011），以及庫門等。 （2011年）。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通常比處理一個目標更復雜。因此，許多方法的開發，以多重目標轉換成單一目標。在一個單一的目標模式搜索解空間，一些研究人員采用經典的優化方法如線性規劃（LP）。例如，例如，他和kanaroglou（2009）使用LP優化土地利用，集中在土地利用與交通之間的關系。其他一些模型是基于人工智能（AI ）方法。例如， Shiffa等。 （ 2011）采用粒子群優化算法（PSO）優化劃撥土地使用，考慮最大土地適宜性和最小改變土地形狀的成本。在另一項由Semboloni （ 2004）的研究 中，模擬退火（SA）方法被用來優化所需要的設施，住宅和商業區域。這些方法的主要問題是，結果強烈地依賴于考慮到目標或功能用于結合成一個目標的權重。此外，非凸優化的解決方案不能被最小化的線性組合來獲得目標（ Cao等2011） 。此外，決策者希望探索一套替代解決方案，權衡不同的目標并做出相應的決策。找到多個解決方案，使用這種方法，該算法必須運行很多次，希望找到不同的解決方案在每次運行時創造權衡解決方案（DEB等。 2002年）。In some other studies, objectives are optimized simultaneously in multiobjective mode focusing on Pareto front. The concept of Pareto front is properly described in Deb et al. (2002) and Coello Coello et al. (2007). The Pareto set is usually independent of the relative importance of objectives, making it suitable for plex applications such as landuse planning. Many studies on landuse optimization are carried out using Pareto front. For example, Feng and Lin (1999) generated different scenarios of urban land uses for urban planners using multiobjective Cumulative Genetic Algorithm (CGA), having the city zones as spatial units. Objective functions were maximizing the suitability of lands for development and maximizing the patibility of neighbouring zones. Member et al. (2000) used an initiative multiobjective CGA to