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1.
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   

2.
Multi-objective optimization can be used to solve land-use allocation problems involving multiple conflicting objectives. In this paper, we show how genetic algorithms can be improved in order to effectively and efficiently solve multi-objective land-use allocation problems. Our focus lies on improving crossover and mutation operators of the genetic algorithms. We tested a range of different approaches either based on the literature or proposed for the first time. We applied them to a land-use allocation problem in Switzerland including two conflicting objectives: ensuring compact urban development and reducing the loss of agricultural productivity. We compared all approaches by calculating hypervolumes and by analysing the spread of the produced non-dominated fronts. Our results suggest that a combination of different mutation operators, of which at least one includes spatial heuristics, can help to find well-distributed fronts of non-dominated solutions. The tested modified crossover operators did not significantly improve the results. These findings provide a benchmark for multi-objective optimization of land-use allocation problems with promising prospectives for solving complex spatial planning problems.  相似文献   

3.
Spatial optimization techniques are commonly used for regionalization problems, often represented as p-regions problems. Although various spatial optimization approaches have been proposed for finding exact solutions to p-regions problems, these approaches are not practical when applied to large-size problems. Alternatively, various heuristics provide effective ways to find near-optimal solutions for p-regions problem. However, most heuristic approaches are specifically designed for particular geographic settings. This paper proposes a new heuristic approach named Automated Zoning Procedure-Center Interchange (AZP-CI) to solve the p-functional regions problem (PFRP), which constructs regions by combining small areas that share common characteristics with predefined functional centers and have tight connections among themselves through spatial interaction. The AZP-CI consists of two subprocesses. First, the dissolving/splitting process enhances diversification and thereby produces an extensive exploration of the solution space. Second, the standard AZP locally improves the objective value. The AZP-CI was tested using randomly simulated datasets and two empirical datasets with different sizes. These evaluations indicate that AZP-CI outperforms two established heuristic algorithms: the AZP and simulated annealing, in terms of both solution quality and consistency of producing reliable solutions regardless of initial conditions. It is also noted that AZP-CI, as a general heuristic method, can be easily extended to other regionalization problems. Furthermore, the AZP-CI could be a more scalable algorithm to solve computational intensive spatial optimization problems when it is combined with cyberinfrastructure.  相似文献   

4.
Pedestrian navigation at night should differ from daytime navigation due to the psychological safety needs of pedestrians. For example, pedestrians may prefer better-illuminated walking environments, shorter travel distances, and greater numbers of pedestrian companions. Route selection at night is therefore a multi-objective optimization problem. However, multi-objective optimization problems are commonly solved by combining multiple objectives into a single weighted-sum objective function. This study extends the artificial bee colony (ABC) algorithm by modifying several strategies, including the representation of the solutions, the limited neighborhood search, and the Pareto front approximation method. The extended algorithm can be used to generate an optimal route set for pedestrians at night that considers travel distance, the illumination of the walking environment, and the number of pedestrian companions. We compare the proposed algorithm with the well-known Dijkstra shortest-path algorithm and discuss the stability, diversity, and dynamics of the generated solutions. Experiments within a study area confirm the effectiveness of the improved algorithm. This algorithm can also be applied to solving other multi-objective optimization problems.  相似文献   

5.
The identification of regions is both a computational and conceptual challenge. Even with growing computational power, regionalization algorithms must rely on heuristic approaches in order to find solutions. Therefore, the constraints and evaluation criteria that define a region must be translated into an algorithm that can efficiently and effectively navigate the solution space to find the best solution. One limitation of many existing regionalization algorithms is a requirement that the number of regions be selected a priori. The recently introduced max-p algorithm does not have this requirement, and thus the number of regions is an output of, not an input to, the algorithm. In this paper, we extend the max-p algorithm to allow for greater flexibility in the constraints available to define a feasible region, placing the focus squarely on the multidimensional characteristics of the region. We also modify technical aspects of the algorithm to provide greater flexibility in its ability to search the solution space. Using synthetic spatial and attribute data, we are able to show the algorithm’s broad ability to identify regions in maps of varying complexity. We also conduct a large-scale computational experiment to identify parameter settings that result in the greatest solution accuracy under various scenarios. The rules of thumb identified from the experiment produce maps that correctly assign areas to their ‘true’ region with 94% average accuracy, with nearly 50% of the simulations reaching 100% accuracy.  相似文献   

6.
The aim of Land-use Suitability Analysis and Planning Problem (LSAPP) is to identify the most suitable parcels of land for future land-uses considering several conflicting criteria. LSAPP can be modeled using a variant of a well-known combinatorial optimization problem called Quadratic Assignment Problem (QAP). In this paper, a multi-objective mathematical model is developed for LSAPP based on QAP modeling. The large-size instances of the proposed multi-objective mathematical model are difficult to solve in a reasonable CPU time using exact algorithms. So, an efficient three-phase hybrid solution procedure is proposed. In the first phase, the compensatory objectives are integrated using Analytic Hierarchy Process (AHP) and Decision-Making Trial and Evaluation Laboratory. Then, based on the aforementioned suitability objective function and other spatial objectives and constraints, a multi-objective LSAPP is constructed. Finally, a hybrid multiple objective meta-heuristic algorithm is proposed to solve the LSAPP. The core of the proposed algorithm is based on Scatter Search while Tabu Search and Variable Neighborhood Search are also utilized. The proposed algorithm is equipped with the concepts of Pareto optimality and Veto Threshold, which improve its efficacy. The proposed algorithm is applied on a real LSAPP case study, in ‘Persian Gulf Knowledge Village’, wherein its performance is compared with a well-known evolutionary computation algorithm called Vector Evaluated Genetic Algorithm (VEGA) using comprehensive statistical analysis. A survey on time complexity of the proposed algorithm is also accomplished. The results show that MOSVNS is significantly superior to VEGA both in single and in multi-objective modes. Furthermore, analysis of time complexity of the proposed algorithm shows that it is of polynomial time and can be applied to significantly larger problems with multiple compensatory and non-compensatory objectives.  相似文献   

7.
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   

8.
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   

9.
多叉树蚁群算法及在区位选址中的应用研究   总被引：3，自引：0，他引：3  
赵元  张新长  康停军 《地理学报》2011,66(2):279-286
本文提出了基于多叉树蚁群算法(ant colony optimization based on multi-way tree) 的区 位选址优化方法。在多目标和大型空间尺度约束条件下,地理区位选址的解决方案组合呈现 海量规模、空间搜索量庞大,难以求出理想解。基于多叉树的蚁群算法对地理空间进行多叉树划分,在多叉树的层上构造蚂蚁路径(ant path),让蚂蚁在多叉树的搜索路径上逐步留下信息 素,借助信息素的通讯来间接协作获得理想的候选解。采用该方法用于广州市的地理区位选址,取得良好结果。实验结果表明：采用基于多叉树的蚁群算法,改善了蚂蚁在空间搜索能 力,适合求解大规模空间下的区位选址问题。  相似文献   

10.
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   

11.
Optimal location search is frequently required in many urban applications for siting one or more facilities. However, the search may become very complex when it involves multiple sites, various constraints and multiple‐objectives. The exhaustive blind (brute‐force) search with high‐dimensional spatial data is infeasible in solving optimization problems because of a huge combinatorial solution space. Intelligent search algorithms can help to improve the performance of spatial search. This study will demonstrate that genetic algorithms can be used with Geographical Information systems (GIS) to effectively solve the spatial decision problems for optimally sitting n sites of a facility. Detailed population and transportation data from GIS are used to facilitate the calculation of fitness functions. Multiple planning objectives are also incorporated in the GA program. Experiments indicate that the proposed method has much better performance than simulated annealing and GIS neighborhood search methods. The GA method is very convenient in finding the solution with the highest utility value.  相似文献   

12.
Optimizing land use allocation is a challenging task, as it involves multiple stakeholders with conflicting objectives. In addition, the solution space of the optimization grows exponentially as the size of the region and the resolution increase. This article presents a new ant colony optimization algorithm by incorporating multiple types of ants for solving complex multiple land use allocation problems. A spatial exchange mechanism is used to deal with competition between different types of land use allocation. This multi-type ant colony optimization optimal multiple land allocation (MACO-MLA) model was successfully applied to a case study in Panyu, Guangdong, China, a large region with an area of 1,454,285 cells. The proposed model took only about 25 minutes to find near-optimal solution in terms of overall suitability, compactness, and cost. Comparison indicates that MACO-MLA can yield better performances than the simulated annealing (SA) and the genetic algorithm (GA) methods. It is found that MACO-MLA has an improvement of the total utility value over SA and GA methods by 4.5% and 1.3%, respectively. The computation time of this proposed model amounts to only 2.6% and 12.3%, respectively, of that of the SA and GA methods. The experiments have demonstrated that the proposed model was an efficient and effective optimization technique for generating optimal land use patterns.  相似文献   

13.
程敏  崔晓 《地理科学》2018,38(12):2049-2057
综合考虑政府、居民、投资者3方需求,构建带约束多目标的养老机构配置优化模型,基于改进免疫算法和GIS技术,对上海市虹口区养老机构的配置优化问题进行研究,分析研究区现有养老机构在空间分布和规模配置上的合理性,提出优化配置方案。研究表明：研究区现有养老机构数量缺口较大、部分养老机构偏离最佳区位、规模与需求存在较大差距;3所位于江湾镇街道的养老机构在现有区位运行欠合理;为充分满足居民养老需求,需在虹口区南部地区增设15所养老机构;通过与一般免疫算法、遗传算法、粒子群算法、模拟退火算法得到的优化结果对比可知,改进免疫算法在此优化问题中的求解效率分别提高45%,38.89%,21.43%,46.34%,求解精度分别提高1.61%,2.73%,5.80%,6.91%。  相似文献   

14.
不同生态保育尺度下铜陵市土地利用结构优化   总被引：2，自引：0，他引：2  
冯长春  曹敏政  谢婷婷 《地理研究》2014,33(12):2217-2227
与传统发展模式相比,新阶段的土地利用应该与区域自然基础和生态环境格局相匹配,科学判别和保护土地空间增长的生态底线,引导地区土地利用结构的优化具有重要意义。以铜陵市为例,从生态环境约束角度出发,利用土地利用的生态环境敏感性评价方法对区域用地进行分等定级,设定了不同生态保育尺度和不同发展情景下的用地约束界限。在此基础上对铜陵市土地利用结构进行多目标情景分析与优化配置研究,从保障生态环境约束并实现效益最优的角度提出铜陵市土地利用结构优化的方案。  相似文献   

15.
Many issues in the definition of regions remain unresolved, whether the regions are to be used in academic work or in practical applications. In the latter, regions defined on two or more criteria are frequently required. A problem then arises in the selection of the regionalization when the optimum solution on one criterion does not coincide with the optima on the others. In such cases, a trade off must be made between the various criteria. A method for making such trade offs is described here in the context of a particular problem: the definition of constituencies for parliamentary and other elections. The program developed for this task can be used both to suggest solutions to a regionalization problem and to evaluate the impact of a regionalization selected by other means.  相似文献   

16.
A set of problems related to formation of areas homogeneous in hydrological aspect is considered. A methodology of regionalization is proposed. New analytical methods of the methodology are intended of solving such problems as (i) the problem of regionalization and (ii) the problem of constructing of a classification for river drainage basins. Efficiency of the methods proposed has been tested on the problems, which arise in the process of constructing classifications for river drainage basins, and also on the problem of territorial regionalization of Japan islands. Investigations, which presume obtaining a set of morphometric and hydrographic characteristics of catchment areas, allow one to study conditions of extreme inundations. It has been discovered that boundaries of the areas have striking similarities in the spatial location of geological structures. Furthermore, these are coincide with the boundaries of the main tectonic fields.  相似文献   

17.
戴特奇  王梁  张宇超  廖聪 《地理科学进展》2016,35(11):1352-1359
在城镇化和农村人口减少的背景下,中国农村地区大量学校撤并,如何优化学校布局成为研究的重点。2008年原建设部发布了农村学校的最小和最大学生规模标准,但该标准对学校布局的影响尚缺乏研究。本文在包含最大距离约束的P-中值模型中增加了学校规模约束,构建了学校布局优化模型,并以北京延庆区小学布局为例,采用分支界定算法进行了求解。结果表明：实施学校规模标准化对学校优化选址有显著的影响,在优化模型中加入学校规模约束后,有65.22%的学校位置发生了改变,呈更加分散型布局;但在乡镇尺度下考察,学校的空间格局则基本未发生变化;学校规模标准化带来的距离增长较小,平均每个学生上学距离仅增长了135 m。并根据结果进一步讨论了研究结果对学校布局优化的政策启示。  相似文献   

18.
Location siting is an important part of service provision, with much potential to impact operational efficiency, safety, security, system reliability, etc. A class of location models seeks to optimize coverage of demand for service that is continuously distributed across space. Decision-making and planning contexts include police/fire resource allocation for a community, siting cellular towers to support cell phone signal transmission, locating emergency warning sirens to alert the public of severe weather and other related dangers, and many others as well. When facilities can be sited anywhere in continuous space to provide coverage to an entire region, this is a very computationally challenging problem to solve because potential demand for service is everywhere and there are an infinite number of potential facility sites to consider. This article develops a new parallel solution approach for this location coverage optimization problem through an iterative bounding scheme on multi-core architectures. The developed approach is applied to site emergency warning sirens in Dublin, Ohio, and fire stations in Elk Grove, California. Results demonstrate the effectiveness and efficiency of the proposed approach, enabling real-time analysis and planning. This work illustrates that the integration of cyberinfrastructure can significantly improve computational efficiency in solving challenging spatial optimization problems, fitting the themes of this special issue: cyberinfrastructure, GIS, and spatial optimization.  相似文献   

19.
Water monitoring networks are generally classified into surface water, precipitation, groundwater or water quality monitoring networks. The design of these networks typically occurs in isolation from each other. We present a regionalization approach to identify homogeneous subregions of large basins that are suitable as areas for the optimization of an integrated water monitoring network. The study area, which comprises a portion of the St. Lawrence Basin, was spatially divided using ecological units. For each ecological unit, 21 attributes were derived including both environmental and hydrological indicators. A spatially constrained regionalization technique was applied to define the final regions. A scree plot was used to determine the number of regions. The sensitivity of the technique to the correlation in the attribute data was removed by utilizing principal component analysis to reduce correlation between attribute data. During regionalization, the component values were weighted by their proportion of the total variance explained. The four regions in the final configuration had areas from 19% to 31% of the total area, 63,597 km2. For the St. Lawrence Basin, this approach is effective for defining homogeneous regions that can be used in further research on the optimization of integrated water monitoring networks. The approach is portable to other regions and can incorporate any set of attribute data that is valuable to the regionalization objective.  相似文献   

20.
In this paper, we report efforts to develop a parallel implementation of the p-compact regionalization problem suitable for multi-core desktop and high-performance computing environments. Regionalization for data aggregation is a key component of many spatial analytical workflows that are known to be NP-Hard. We utilize a low communication cost parallel implementation technique that provides a benchmark for more complex implementations of this algorithm. Both the initialization phase, utilizing a Memory-based Randomized Greedy and Edge Reassignment (MERGE) algorithm, and the local search phase, utilizing Simulated Annealing, are distributed over available compute cores. Our results suggest that the proposed parallelization strategy is capable of solving the compactness-driven regionalization problem both efficiently and effectively. We expect this work to advance CyberGIS research by extending its application areas into the regionalization world and to make a contribution to the spatial analysis community by proposing this parallelization strategy to solve large regionalization problems efficiently.  相似文献   

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