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1.
水质模型参数的非数值随机优化   总被引：4，自引：2，他引：4  
郑红星  李丽娟 《地理研究》2001,20(1):97-102
以模拟退火算法为核心着重讨论了水质模型参数的非数值随机优化方法。实例分析表明,利用非数值随机优化方法(包括模拟退火算法和遗传算法)对水质模型参数进行估计,可以获得较为理想的结果。不同参数估计方法的比较进一步阐述了非数值随机优化方法在参数估计中的优点  相似文献   

2.
基于多目标遗传算法的土地利用空间结构优化配置   总被引：12，自引：0，他引：12  
针对如何将土地利用数量结构落实到具体的地域空间，以实现土地资源的优化配置的土地利用总体规划编制难点，以及常规的叠加法等土地利用配置方法难以根据土地适宜性评价结果，有效地将土地利用数量结构匹配到具体的土地单元问题，该文利用遗传算法的内在并行机制及其全局优化的特性，提出了基于多目标遗传算法的土地利用空间结构优化配置方法。实例分析表明，该方法具有客观性强、灵活性高、操作简便等优点。  相似文献   

3.
Limited development ecological zones (LDEZs) are often located in poverty-stricken, ecologically vulnerable areas where ethnic minorities reside. Studies on optimal spatial land-use allocation in LDEZs can promote economic and intensive land use, improve soil quality, facilitate local socioeconomic development, and maintain environmental stability. In this study, we optimized spatial land-use allocations in an LDEZ using the geographic information system (GIS) and a genetic ant colony algorithm (GACA). The multi-objective function considers economic benefits and ecological green equivalents, and improves soil erosion. We developed the GACA by integrating a genetic algorithm (GA) with an ant colony algorithm (ACA). This avoids a large number of redundant iterations and the low efficiency of the GA, and the slow convergence speed of the ACA. The study area is located in Pengyang County, Ningxia, China, which is a typical LDEZ. The land-use data were interpreted from remote sensing (RS) images and GIS. We determined the optimal spatial land-use allocations in the LDEZ using the GACA in the GIS environment. We compared the original and optimal spatial schemes in terms of economic benefits, ecological green equivalents, and soil erosion. The results of the GACA were superior to the original allocation, the ACA, and the multi-objective genetic algorithm, in terms of the optimum, time, and robust performance indexes. We also present some suggestions for the reasonable development and protection of LDEZs.  相似文献   

4.
Allocation for earthquake emergency shelters is a complicated geographic optimization problem because it involves multiple sites, strict constraints, and discrete feasible domain. Huge solution space makes the problem computationally intractable. Traditional brute-force methods can obtain exact optimal solutions. However, it is not sophisticated enough to solve the complex optimization problem with reasonable time especially in high-dimensional solution space. Artificial intelligent algorithms hold the promise of improving the effectiveness of location search. This article proposes a modified particle swarm optimization (PSO) algorithm to deal with the allocation problem of earthquake emergency shelter. A new discrete PSO and the feasibility-based rule are incorporated according to the discrete solution space and strict constraints. In addition, for enhancing search capability, simulated annealing (SA) algorithm is employed to escape from local optima. The modified algorithm has been applied to the allocation of earthquake emergency shelters in the Zhuguang Block of Guangzhou City, China. The experiments have shown that the algorithm can identify the number and locations of emergency shelters. The modified PSO algorithm shows a better performance than other hybrid algorithms presented in the article, and is an effective approach for the allocation problem of earthquake emergency shelters.  相似文献   

5.
Rural land use development is experiencing a transition stage of socioeconomic and land use development in China. Historic land use transition process and policy interventions have key influence on the applicability of land use allocation solutions in future land use management. Strategic land use allocation is therefore required to possess a good adjustment capability to the transition process. Although heuristic optimization methods have been promising to solve land use allocation problems, most of them ignored the spatially explicit effect of historic land use transition and policies. To help resolve this issue, this study aims to optimize future land use pattern in the context of rural land use development. We took Yunmeng County, one of the typical major grain producing and rapidly urbanizing areas in central China, as a case study and solved the sustainable land use allocation problem by using an improved heuristic optimization model. The model was constructed based on the integration of a spatial discrete particle swarm optimization and cellular automata-Markov simulation approach. The spatiotemporal land use patterns and policy interventions were represented by the CA-Markov as in spatially explicit transition rules, and then incorporated into the discrete PSO for optimal land use solutions. We examined the influence of the joint effect of spatiotemporal land use patterns and policy interventions on the land use allocation outcome. Our results demonstrate the robustness and potential of the proposed model, and, more importantly, indicate the significance of incorporating the spatiotemporal land use patterns and policy interventions into rural land use allocation.  相似文献   

6.
Spatial optimization is complex because it usually involves numerous spatial factors and constraints. The optimization becomes more challenging if a large set of spatial data with fine resolutions are used. This article presents an agent-based model for optimal land allocation (AgentLA) by maximizing the total amount of land-use suitability and the compactness of patterns. The essence of the optimization is based on the collective efforts of agents for formulating the optimal patterns. A local and global search strategy is proposed to inform the agents to select the sites properly. Three sets of hypothetical data were first used to verify the optimization effects. AgentLA was then applied to the solution of the actual land allocation optimization problems in Panyu city in the Pearl River Delta. The study has demonstrated that the proposed method has better performance than the simulated annealing method for solving complex spatial optimization problems. Experiments also indicate that the proposed model can produce patterns that are very close to the global optimums.  相似文献   

7.
Land-use allocation is of great importance for rapid urban planning and natural resource management. This article presents an improved artificial bee colony (ABC) algorithm to solve the spatial optimization problem. The new approach consists of a heuristic information-based pseudorandom initialization (HIPI) method for initial solutions and pseudorandom search strategy based on a long-chain (LC) mechanism for neighborhood searches; together, these methods substantially improve the search efficiency and quality when handling spatial data in large areas. We evaluated the approach via a series of land-use allocation experiments and compared it with particle swarm optimization (PSO) and genetic algorithm (GA) methods. The experimental results show that the new approach outperforms the current methods in both computing efficiency and optimization quality.  相似文献   

8.
Traveling salesman problem (TSP) and its quasi problem (Quasi-TSP) are typical problems in path optimization, and ant colony optimization (ACO) algorithm is considered as an effective way to solve TSP. However, when the problems come to high dimensions, the classic algorithm works with low efficiency and accuracy, and usually cannot obtain an ideal solution. To overcome the shortcoming of the classic algorithm, this paper proposes an improved ant colony optimization (I-ACO) algorithm which combines swarm intelligence with local search to improve the efficiency and accuracy of the algorithm. Experiments are carried out to verify the availability and analyze the performance of I-ACO algorithm, which cites a Quasi-TSP based on a practical problem in a tourist area. The results illustrate the higher accuracy and efficiency of the I-ACO algorithm to solve Quasi-TSP, comparing with greedy algorithm, simulated annealing, classic ant colony algorithm and particle swarm optimization algorithm, and prove that the I-ACO algorithm is a positive effective way to tackle Quasi-TSP.  相似文献   

9.
土地利用结构多目标优化遗传算法   总被引：2，自引：0，他引：2  
传统数学方法难以有效解决土地利用结构多目标优化问题,针对土地利用结构优化的多目标性和遗传算法在多目标优化求解方面的优势,以经济效益和生态效益为目标函数建立土地利用结构优化数学模型,采用遗传算法进行模型求解.以重庆市合川区为例详细介绍了具体应用过程,得出了4个可行方案,对方案进行论证择优,选出满意的最佳方案,证明采用遗传算法进行土地利用结构多目标优化求解是科学可行的.  相似文献   

10.
Conventional methods have difficulties in forming optimal paths when raster data are used and multi‐objectives are involved. This paper presents a new method of using ant colony optimization (ACO) for solving optimal path‐covering problems on unstructured raster surfaces. The novelty of this proposed ACO includes the incorporation of a couple of distinct features which are not present in classical ACO. A new component, the direction function, is used to represent the ‘visibility’ in the path exploration. This function is to guide an ant walking toward the final destination more efficiently. Moreover, a utility function is proposed to reflect the multi‐objectives in planning applications. Experiments have shown that classical ACO cannot be used to solve this type of path optimization problems. The proposed ACO model can generate near optimal solutions by using hypothetical data in which the optimal solutions are known. This model can also find the near optimal solutions for the real data set with a good convergence rate. It can yield much higher utility values compared with other common conventional models.  相似文献   

11.
The optimal spatial allocation of irrigation water under uncertainty has become a serious concern because of irrigation water shortage and uncertain factors that affect irrigation water allocation. In this study, an optimal multi-objective model for irrigation water allocation under uncertainty is developed to maximise the economic benefit of crops and minimise the operation cost and water deficit of crop irrigation. The original and optimal plantation structure, irrigation mode and soil water content are acquired through geospatial technology. A bilayer nested optimisation (BLNO) algorithm is designed to produce multiple individuals of design vectors using an ant colony neural network algorithm for an outer optimisation. Meanwhile, a continuous adaptive ant colony (CAAC) algorithm is used for inner optimisation to calculate the interval values of the uncertain model. The crop distribution and irrigation mode are obtained to parameterise the planting area and the water demand of each crop and each block in the multi-objective model. This model is then solved and analysed. Compared to the optimal schemes obtained from an inexact two-stage fuzzy-stochastic programming and the CAAC, respectively, BLNO can effectively and efficiently solve the optimal spatial allocation of irrigation water under uncertainty. This method can spatially maximise the economic benefit of crops and minimise the operation cost and water deficit of crop irrigation using lower and upper bound maps whilst visually obtaining the exact crop type, reasonable irrigation method and precise water demand for each block and for the entire irrigated area.  相似文献   

12.
A spatial multi-objective land use optimization model defined by the acronym ‘NSGA-II-MOLU’ or the ‘non-dominated sorting genetic algorithm-II for multi-objective optimization of land use’ is proposed for searching for optimal land use scenarios which embrace multiple objectives and constraints extracted from the requirements of users, as well as providing support to the land use planning process. In this application, we took the MOLU model which was initially developed to integrate multiple objectives and coupled this with a revised version of the genetic algorithm NSGA-II which is based on specific crossover and mutation operators. The resulting NSGA-II-MOLU model is able to offer the possibility of efficiently searching over tens of thousands of solutions for trade-off sets which define non-dominated plans on the classical Pareto frontier. In this application, we chose the example of Tongzhou New Town, China, to demonstrate how the model could be employed to meet three conflicting objectives based on minimizing conversion costs, maximizing accessibility, and maximizing compatibilities between land uses. Our case study clearly shows the ability of the model to generate diversified land use planning scenarios which form the core of a land use planning support system. It also demonstrates the potential of the model to consider more complicated spatial objectives and variables with open-ended characteristics. The breakthroughs in spatial optimization that this model provides lead directly to other properties of the process in which further efficiencies in the process of optimization, more vivid visualizations, and more interactive planning support are possible. These form directions for future research.  相似文献   

13.
Selecting the set of candidate viewpoints (CVs) is one of the most important procedures in multiple viewshed analysis. However, the quantity of CVs remains excessive even when only terrain feature points are selected. Here we propose the Region Partitioning for Filtering (RPF) algorithm, which uses a region partitioning method to filter CVs of a multiple viewshed. The region partitioning method is used to decompose an entire area into several regions. The quality of CVs can be evaluated by summarizing the viewshed area of each CV in each region. First, the RPF algorithm apportions each CV to a region wherein the CV has a larger viewshed than that in other regions. Then, CVs with relatively small viewshed areas are removed from their original regions or reassigned to another region in each iterative step. In this way, a set of high-quality CVs can be preserved, and the size of the preserved CVs can be controlled by the RPF algorithm. To evaluate the computational efficiency of the RPF algorithm, its performance was compared with simple random (SR), simulated annealing (SA), and ant colony optimization (ACO) algorithms. Experimental results indicate that the RPF algorithm provides more than a 20% improvement over the SR algorithm, and that, on average, the computation time of the RPF algorithm is 63% that of the ACO algorithm.  相似文献   

14.
土地利用优化配置中系列模型的应用——以乐清市为例   总被引：44，自引：12，他引：32  
土地利用优化配置,既包括宏观数量与空间结构格局的优化,也包括微观尺度生产要素的合理比配,是一个多目标、多层次的持续拟合与决策过程。本文结合乐清市实证研究,提出了运用系列模型研究县域土地利用优化配置的新方法。系列模型由空间分区模型、结构优化模型和微观设计模型,按照土地资源优化配置目标的内在联系性组合而成。系列模型既能够发挥单个模型的作用,也能充分利用它们在土地利用优化配置与决策中所具有的同一性与互补性,在科学协调土地利用配置数量与空间、宏观与微观之间的关系中更好地发挥其综合优势,因而具有广阔的应用前景。  相似文献   

15.
多智能体区域土地利用优化配置模型及其应用   总被引：12，自引：0，他引：12  
土地利用优化配置对促进区域可持续土地利用具有重要意义,然而现有的土地利用优化配置模型引导可持续土地利用的能力有待提高。本文从整体考量区域土地利用优化配置数量、空间、时间三维特征的角度,定义了区域土地利用优化配置多智能体系统及其决策行为规则,构建了基于多智能体系统的区域土地利用优化配置RLUOA (Regional Land Use OptimizationAllocation) 模型,并以中部地区典型城市--长沙市为例开展了实证应用研究。研究结果表明：该模型能够将规划时间段内多目标约束的区域土地利用规模的数量结构合理配置到不同的空间单元,实现土地利用数量结构、空间布局、效益的协同优化,构建整体上经济可行、社会可接受、生态环境友好的土地利用格局,并明显提高区域整体土地利用经济、生态和社会效益,从而能够为促进区域土地资源可持续利用和制定土地利用总体规划提供参考借鉴。  相似文献   

16.
戴芹  刘建波 《地理研究》2009,28(4):1136-1145
蚁群算法作为一种新型的智能优化算法,已经成功应用在许多领域,然而应用蚁群优化算法进行遥感数据处理则是一个新的研究热点。蚁群规则挖掘算法是基于分类规则挖掘进行分类,能够处理多特征的数据。因此,论文将蚁群规则挖掘算法应用到多特征遥感数据分类处理中,并采用北京地区的Landsat TM和 Envisat ASAR数据作为实验数据,对选择的遥感数据进行了多特征分类实验。实验结果分别与最大似然分类法、C4.5方法进行对比,分析表明:1)蚁群规则挖掘算法是一种无参数分类的智能方法,具有很好的鲁棒性,2)能够挖掘较简单的分类规则;3)能够充分利用多源遥感数据等。它可以充分利用多特征数据进行土地覆盖分类,从而能够提高分类的效率。  相似文献   

17.
中国北方农牧交错带土地利用空间优化布局的动态模拟   总被引：2，自引：0，他引：2  
合理、最优的土地利用方案涉及到影响土地利用变化的诸多因素,引起了目前学术界的广泛关注与重视。 本文利用CLUE- S 模型的理论框架,构建了中国北方农牧交错带土地利用空间优化布局模拟模型,通过对中国北 方农牧交错带土地利用变化的情景设置,实现了六种土地利用变化分配模式下的空间优化布局。研究表明,中国北 方农牧交错带在不同的分配模式下,各用地类型间的竞争导致区域土地利用类型之间的转移差异较大。经过空间 优化的土地利用空间分布和土地利用现状相比较,耕地和草地更加集中,从不同分配模式的空间分布比较中可以 看出,进行土地利用空间分配时,土地利用变化的敏感地带位于东北部地区和西部地区,东北部地区主要表现为林 地转化为耕地,在西北地区,主要是草地和耕地之间的相互转化,而在广大的中部地区,则主要是耕地转化为草地。  相似文献   

18.
针对现阶段各规划边界交叉、空间重叠的问题,本文从提高空间价值、减少空间破碎度和协调各类空间的角度出发,在梳理现有空间优化模型及智能算法的基础上,改进多目标规划模型并适应性改造遗传算法,以此二者构建市级国土空间优化模型。以烟台市为例,设置3种情景为决策者提供方案集进行3类规划主导下的2020年国土空间优化研究,结果显示：①优化后烟台市农业、城镇和生态空间价值分别增加了23.24%、29.27%、6.30%;②不同情景下空间分布合理且较为集中,生态保护情景有利于国土空间集合连片,经济发展情景更适合于多空间协调发展。研究表明：该模型能有效地解决国土空间内容重叠问题,明显提高了国土空间价值,同时优化模型适应性强,为“多规合一”背景下市级国土空间优化提供技术支撑。  相似文献   

19.
The accurate location and allocation of disaster emergency shelters are key components of effective urban planning and emergency management. Various models have been developed to solve the location-allocation problem, but gaps remain with regard to model realism and associated applicability. For the available location and allocation models of earthquake emergency shelters, uncertainty with respect to earthquake hazard, population exposure, rate of damage to buildings and the effects of evacuee behavior are often neglected or oversimplified. Moreover, modifying the models can be an alternative means of improving the solution quality when the optimization algorithm has difficulty coping with a complex, high-dimensional problem. This article develops a scenario-based hybrid bilevel model that addresses the concerns related to high-dimensional complexity and provides a higher degree of realism by incorporating the uncertainties of population dynamics and earthquake damage scenarios into location-allocation problems for earthquake emergency shelters. A modified particle swarm optimization algorithm combined with a simulated annealing algorithm was applied to derive solutions using the hybrid bilevel model and a conventional multi-objective model, and the solutions obtained using the two models were then compared. The novel features of the study include the hybrid bilevel model that considers the dynamic number of evacuees and its implementation for earthquake emergency shelter location and allocation. The results show that the solutions significantly differ between daytime and nighttime. When applied to the multi-objective model, the optimization algorithm is time consuming and may only find the local optima and provide suboptimal solutions in the considered scenarios with more evacuees. By contrast, the hybrid bilevel model shows more desirable performance because it significantly reduces the dimensionality of the location-allocation problem based on a two-step-to-reach approach. The proposed hybrid bilevel model is proven to be useful for optimal shelter allocation, and the presented results can be used as a reference for balancing the interests of the government and residents during the planning of shelters in Beijing.  相似文献   

20.
The spatial allocation of water resources is optimised using the multi-objective functions and multi-constrained conditions of the Pareto ant colony algorithm (PACA). The objective function is the highest benefit to the economy, society and the environment, while the constraints include water supply, demand and quality. The PACA is improved by limiting local pheromone scope and dynamically updating global pheromone levels. Since both strategies guide the ant towards borders of high-pheromone concentration, the new approach enhances the global search capability and convergence speed. Programming, database management and interface tools are then integrated into geographic information systems (GIS) software. The study area is located in Zhenping County, Henan Province, China, and water resource data are obtained using remote sensing (RS) and GIS technology. The improved PACA is solved in the GIS environment. Optimal spatial allocation schemes are obtained for surface, ground and transferred water and the model yields optimal spatial benefit schemes of water resources, embracing economic, social and ecological benefits. The results of improved PACA are superior to those of other intelligent optimisation algorithms, including the ant colony algorithm, multi-objective genetic algorithm and back-propagation artificial neural network. Therefore, the integration of RS, GIS and PACA can effectively optimise the large-scale, multi-objective allocation of water resources. The model also enhances the global search capability, convergence speed and result precision, and can potentially solve other optimal spatial problems with multi-objective functions.  相似文献   

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