基于多目标蚁群算法的土地利用优化配置
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摘要
土地利用优化配置是促进土地资源的节约和集约利用,实现土地利用可持续发展的重要手段,也是当前土地科学和土地资源管理工作面临的重要课题。如何制定既科学又具可操作性的土地利用配置方案,避免规划陷入特定的数字游戏,使得规划真正得到贯彻实施,是土地利用优化配置迫切需要研究和解决的关键问题。本文将围绕土地利用优化配置这一核心问题,针对现有的绝大多数模型方法侧重于数量结构优化,而缺乏科学和合理的空间配置方法,导致规划的数字所对应的对象往往无法真正地落实到空间的问题,提出一种新的土地利用优化配置模型—蚁群算法,并将该算法与GIS相结合,在一定程度上确保了数量结构优化与空间布局优化于一体。
利用蚁群算法,围绕土地利用优化配置这一研究目标,本论文开展了相关的研究工作,主要研究的内容包括以下几个方面：
(1)详细介绍土地利用空间优化配置理论研究、技术研究、群体智能优化以及蚁群算法的国内外研究进展及应用,通过对国内外研究现状进行详细综述,发现已有研究存在的问题,进而根据土地利用优化配置的内涵和特性所在,明确了土地利用优化配置研究的思路,把可持续发展理论、生态经济理论、系统论和景观生态学论、多目标优化的方法、蚁群算法等作为土地利用优化配置的理论和方法基础。
(2)建立优化配置模型的多目标体系和约束体系。具体内容包括：阐述村土地可持续利用评价指标体系构建的基本思路、基本原则、建立与土地利用规划相结合的土地可持续利用评价指标体系的框架以及协调度评价模型,并介绍了土地利用空间优化的最小成本规划模型和空间集聚的函数模型,将土地可持续利用协调度评价模型与其它目标函数共同构建多目标优化体系,提出了土地利用空间优化的约束体系包括数量约束体系和土地利用类型转换约束,并对土地利用类型的转换作了详细分析,给出了土地利用转换一般规则体系。最后设计土地利用优化配置整合模型,使得土地可持续利用的目标与空间布局目标相辅相成,数量约束和空间格局约束共同作用,从而达到整体优化的目的。
(3)详细介绍了改进蚁群算法土地利用优化模型的设计思路。首先对基本蚁群算法的核心思想以及基本蚁群算法进行介绍,然后针对基本蚁群算法存在的缺陷提出了改进的措施,并针对土地利用优化配置这一复杂问题的要求,详细介绍了算法具体改进方法并对改进后蚁群算法进行详细说明,包括算法的各个参数,如种群规模、启发因子、信息素挥发因子等参数,以及对多目标和约束条件的处理等进行详细的说明；随后,将多目标蚁群算法与GIS耦合建立多目标的土地利用优化配置模型,以满足了土地利用数据的空间特性和要求,并对其中关键的操作步骤进行详细的介绍,如编码框架的确定,初始蚂蚁的生成、目标函数的建立、启发信息函数的建立、信息素更新规则的建立、选择概率函数的设立等。
(4)在基于多目标蚁群算法的土地利用优化配置模型研究基础上,开展了实例应用研究。以湖北省宜城市土地利用总体规划修编数据为依据,选取举有代表性的乡镇(郑集镇)作为实验区。首先对实验区土地利用现状结构和空间格局以及土地可持续利用的现状进行评价；然后,以土地利用现状为基础,根据多目标蚁群优化算法与GIS耦合的土地利用空间优化模型的要求,研究了实验区土地利用优化模型的具体目标函数、约束条件以及模型数据的处理问题,最后对不同参数组合对算法的影响进行分析和优化计算,并确定该区域的土地利用优化方案,并把该方案与基本蚁群算法以及遗传算法的优化方案进行比较,对基于改进后的多目标蚁群算法的方法正确性、有效性进行验证；将土地利用优化结果与现状利用状况进行了对比分析,验证了本研究提出的多目标的蚁群算法与GIS的耦合模型在可行性及其在空间布局上的优势。
The optimized allocation of land use is not only an important method to accelerate land intensive use and make land sustainable use come true, but also a hot point that the land science and the management currently faces. How to formulate a scientific and feasible planning and avoid falling into a specific digital game is the key issues to be solved. This paper will focus on the core problem-the optimal allocation of land use, by reviewing the existing model methods, most them focused on the number of land use structure optimization and lacked researches in the optimization allocation for spatial pattern of land use, which caused the planning achievements not to be properly carried out in the space.This paper will propose a new optimizing allocation model-ant colony algorithm, which combinates with GIS, will guarantee the coherence between the quantity and the space in certain degree.
The paper focus around the study goal-the optimal allocation of land use, starts the relevant work by using ant colony algorithm, the main contents of the study include the following aspects:
Firstly, The article introduces detailedly the land-use theory of optimal allocation of space research, technology research, the progress of the swarm intelligent optimization、ant colony algorithm and its main applications in different fields. By summarizing the documents home and abroad, the paper points out the existent problems in current researchs, then according to the meaning and characteristic, forms the research process of land use allocation. This section also introduces the basic theories and methods related to this article:the sustainable development thoery, the system theory, the ecology economic theory, landscape ecology, and the method of multi-objective optimization as well as the intelligence optimization method, the antcolony algorithm,etc.
Secondly, the article proposed a multiobject model for land use optimization, the specific contents contain:introducing the basic ideas, basic principles for construction of the evaluation index system of land sustainable use, build an evaluation index frame of land sustainable use and the evaluation modle. This article also introduced the minimum cost planning model and the compactness model based on spatial clustering, according to the three models to construct system for multi-objective optimization. Bring forward the constraints system for space optimization (including the number of constraint system and conversion bindings of land-use type), and analying detaily the conversion of land use types and making the general rules of land-use conversion system. Finally, this article design the optimization integrated model of land use allocation, combinating the effectiveness of the traditional goals with the objectives of the space layout, the number of binding constraints and the joint effect of spatial pattern to the overall purpose of optimization.
Thirdly, the article introduce detaily the design idea of the multiobjective ant colony optimization model. At first, the article introduce the core idea of basic ant colony and adaptive ant colony algorithm, then in order to adapt land use optimization allocation, we do some improvement aims to the limitation of the basic model, and design and explanation detaily for the improved multiobjective ACO, includes each parameter of the model, such as population size, heuristic factor, evaporation factor of pheromone, ect, and also contains how to deal with multiobject and the constraints. Subsequently, in order to meet the demand of land use data, the improved ACO is coupled with GIS to establish the spatial optimal allocation. This section design detaily the key link, including the coding framework of the ants, the initial formation of ants, the fitness evaluation function for each ant, heuristic information function, pheromone updating rule as well as the selection probability function and so on.
Finally, this article carries out the application based on the land-use optimization model. According to the data of the general land use planning of Yicheng, Hubei province, selecting the representative town-Zhengji as an experimental zone. Firstly, the experiment evaluates the land use structure and spatial pattern. Based on the present land use tructure, according to the requirements of the experimental study of land use optimization model multi-objective ant colony optimization algorithm coupled with the GIS, studying the specific objective functions of the experimental zone, constraints, as well as the issue of data processing model for land use optimization model. Then, the article analyses the optimization results of the town, and compares the land sustainable use optimization with land utilization of status quo to verify the rationality of the classified index-based land sustainable use system and the adaption of the improved ACO optimization. At last, the aricle analyses the effection of different combination of preferences, and selects the best result based on the efficiency of algorithm, compares the result with GA optimization program and the basic ant colony algorithm to verify the advantage of improved multi-objective self-adapt ACO, which shows that the coupled model ACO and GIS is feasible in land use optimization allocation.
利用蚁群算法,围绕土地利用优化配置这一研究目标,本论文开展了相关的研究工作,主要研究的内容包括以下几个方面：
(1)详细介绍土地利用空间优化配置理论研究、技术研究、群体智能优化以及蚁群算法的国内外研究进展及应用,通过对国内外研究现状进行详细综述,发现已有研究存在的问题,进而根据土地利用优化配置的内涵和特性所在,明确了土地利用优化配置研究的思路,把可持续发展理论、生态经济理论、系统论和景观生态学论、多目标优化的方法、蚁群算法等作为土地利用优化配置的理论和方法基础。
(2)建立优化配置模型的多目标体系和约束体系。具体内容包括：阐述村土地可持续利用评价指标体系构建的基本思路、基本原则、建立与土地利用规划相结合的土地可持续利用评价指标体系的框架以及协调度评价模型,并介绍了土地利用空间优化的最小成本规划模型和空间集聚的函数模型,将土地可持续利用协调度评价模型与其它目标函数共同构建多目标优化体系,提出了土地利用空间优化的约束体系包括数量约束体系和土地利用类型转换约束,并对土地利用类型的转换作了详细分析,给出了土地利用转换一般规则体系。最后设计土地利用优化配置整合模型,使得土地可持续利用的目标与空间布局目标相辅相成,数量约束和空间格局约束共同作用,从而达到整体优化的目的。
(3)详细介绍了改进蚁群算法土地利用优化模型的设计思路。首先对基本蚁群算法的核心思想以及基本蚁群算法进行介绍,然后针对基本蚁群算法存在的缺陷提出了改进的措施,并针对土地利用优化配置这一复杂问题的要求,详细介绍了算法具体改进方法并对改进后蚁群算法进行详细说明,包括算法的各个参数,如种群规模、启发因子、信息素挥发因子等参数,以及对多目标和约束条件的处理等进行详细的说明；随后,将多目标蚁群算法与GIS耦合建立多目标的土地利用优化配置模型,以满足了土地利用数据的空间特性和要求,并对其中关键的操作步骤进行详细的介绍,如编码框架的确定,初始蚂蚁的生成、目标函数的建立、启发信息函数的建立、信息素更新规则的建立、选择概率函数的设立等。
(4)在基于多目标蚁群算法的土地利用优化配置模型研究基础上,开展了实例应用研究。以湖北省宜城市土地利用总体规划修编数据为依据,选取举有代表性的乡镇(郑集镇)作为实验区。首先对实验区土地利用现状结构和空间格局以及土地可持续利用的现状进行评价；然后,以土地利用现状为基础,根据多目标蚁群优化算法与GIS耦合的土地利用空间优化模型的要求,研究了实验区土地利用优化模型的具体目标函数、约束条件以及模型数据的处理问题,最后对不同参数组合对算法的影响进行分析和优化计算,并确定该区域的土地利用优化方案,并把该方案与基本蚁群算法以及遗传算法的优化方案进行比较,对基于改进后的多目标蚁群算法的方法正确性、有效性进行验证；将土地利用优化结果与现状利用状况进行了对比分析,验证了本研究提出的多目标的蚁群算法与GIS的耦合模型在可行性及其在空间布局上的优势。
The optimized allocation of land use is not only an important method to accelerate land intensive use and make land sustainable use come true, but also a hot point that the land science and the management currently faces. How to formulate a scientific and feasible planning and avoid falling into a specific digital game is the key issues to be solved. This paper will focus on the core problem-the optimal allocation of land use, by reviewing the existing model methods, most them focused on the number of land use structure optimization and lacked researches in the optimization allocation for spatial pattern of land use, which caused the planning achievements not to be properly carried out in the space.This paper will propose a new optimizing allocation model-ant colony algorithm, which combinates with GIS, will guarantee the coherence between the quantity and the space in certain degree.
The paper focus around the study goal-the optimal allocation of land use, starts the relevant work by using ant colony algorithm, the main contents of the study include the following aspects:
Firstly, The article introduces detailedly the land-use theory of optimal allocation of space research, technology research, the progress of the swarm intelligent optimization、ant colony algorithm and its main applications in different fields. By summarizing the documents home and abroad, the paper points out the existent problems in current researchs, then according to the meaning and characteristic, forms the research process of land use allocation. This section also introduces the basic theories and methods related to this article:the sustainable development thoery, the system theory, the ecology economic theory, landscape ecology, and the method of multi-objective optimization as well as the intelligence optimization method, the antcolony algorithm,etc.
Secondly, the article proposed a multiobject model for land use optimization, the specific contents contain:introducing the basic ideas, basic principles for construction of the evaluation index system of land sustainable use, build an evaluation index frame of land sustainable use and the evaluation modle. This article also introduced the minimum cost planning model and the compactness model based on spatial clustering, according to the three models to construct system for multi-objective optimization. Bring forward the constraints system for space optimization (including the number of constraint system and conversion bindings of land-use type), and analying detaily the conversion of land use types and making the general rules of land-use conversion system. Finally, this article design the optimization integrated model of land use allocation, combinating the effectiveness of the traditional goals with the objectives of the space layout, the number of binding constraints and the joint effect of spatial pattern to the overall purpose of optimization.
Thirdly, the article introduce detaily the design idea of the multiobjective ant colony optimization model. At first, the article introduce the core idea of basic ant colony and adaptive ant colony algorithm, then in order to adapt land use optimization allocation, we do some improvement aims to the limitation of the basic model, and design and explanation detaily for the improved multiobjective ACO, includes each parameter of the model, such as population size, heuristic factor, evaporation factor of pheromone, ect, and also contains how to deal with multiobject and the constraints. Subsequently, in order to meet the demand of land use data, the improved ACO is coupled with GIS to establish the spatial optimal allocation. This section design detaily the key link, including the coding framework of the ants, the initial formation of ants, the fitness evaluation function for each ant, heuristic information function, pheromone updating rule as well as the selection probability function and so on.
Finally, this article carries out the application based on the land-use optimization model. According to the data of the general land use planning of Yicheng, Hubei province, selecting the representative town-Zhengji as an experimental zone. Firstly, the experiment evaluates the land use structure and spatial pattern. Based on the present land use tructure, according to the requirements of the experimental study of land use optimization model multi-objective ant colony optimization algorithm coupled with the GIS, studying the specific objective functions of the experimental zone, constraints, as well as the issue of data processing model for land use optimization model. Then, the article analyses the optimization results of the town, and compares the land sustainable use optimization with land utilization of status quo to verify the rationality of the classified index-based land sustainable use system and the adaption of the improved ACO optimization. At last, the aricle analyses the effection of different combination of preferences, and selects the best result based on the efficiency of algorithm, compares the result with GA optimization program and the basic ant colony algorithm to verify the advantage of improved multi-objective self-adapt ACO, which shows that the coupled model ACO and GIS is feasible in land use optimization allocation.
引文
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