共查询到20条相似文献，搜索用时 31 毫秒
1.
本文提出了基于遗传算法来寻找CA模型最佳参数的方法。CA被越来越多地应用于城市和土地利用等复杂系统的动态模拟。CA模型中变量的参数值对模拟结果有非常重要的影响。如何获取理想的参数值是模型的关键。传统的逻辑回归模型运算简单,常常用来获取模型的参数值,要求解释变量间线性无关,所以获取的城市CA模型参数具有一定的局限性。遗传算法在参数优化组合、快速搜索参数值方面有很大的优势。本文利用遗传算法来自动获取优化的CA模型参数值,并获得了纠正后的CA模型。将该模型应用于东莞1988~2004年的城市发展的模拟中,得到了较好的效果。研究结果表明,遗传算法可以有效地自动获取CA模型的参数,其模拟的结果要比传统的逻辑回归校正的CA模型模拟精度高。 相似文献
2.
Cellular automata (CA) models are commonly used to model vegetation dynamics, with the genetic algorithm (GA) being one method of calibration. This article investigates different GA settings, as well as the combination of a GA with a local optimiser to improve the calibration effort. The case study is a pattern-calibrated CA to model vegetation regrowth in central Victoria, Australia. We tested 16 GA models, varying population size, mutation rate, and level of allowable mutation. We also investigated the effect of applying a local optimiser, the Nelder?Mead Downhill Simplex (NMDS) at GA convergence. We found that using a decreasing mutation rate can reduce computational cost while avoiding premature GA convergence, while increasing population size does not make the GA more efficient. The hybrid GA-NMDS can also reduce computational cost compared to a GA alone, while also improving the calibration metric. We conclude that careful consideration of GA settings, including population size and mutation rate, and in particular the addition of a local optimiser, can positively impact the efficiency and success of the GA algorithm, which can in turn lead to improved simulations using a well-calibrated CA model. 相似文献
3.
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
4.
Ground vibration induced by rock blasting is one of the most crucial problems in surface mines and tunneling projects. Hence, accurate prediction of ground vibration is an important prerequisite in the minimization of its environmental impacts. This study proposes hybrid intelligent models to predict ground vibration using adaptive neuro-fuzzy inference system (ANFIS) optimized by particle swarm optimization (PSO) and genetic algorithms (GAs). To build prediction models using ANFIS, ANFIS–GA, and ANFIS–PSO, a database was established, consisting of 86 data samples gathered from two quarries in Iran. The input parameters of the proposed models were the burden, spacing, stemming, powder factor, maximum charge per delay (MCD), and distance from the blast points, while peak particle velocity (PPV) was considered as the output parameter. Based on the sensitivity analysis results, MCD was found as the most effective parameter of PPV. To check the applicability and efficiency of the proposed models, several traditional performance indices such as determination coefficient (R2) and root-mean-square error (RMSE) were computed. The obtained results showed that the proposed ANFIS–GA and ANFIS–PSO models were capable of statistically predicting ground vibration with excellent levels of accuracy. Compared to the ANFIS, the ANFIS–GA model showed an approximately 61% decrease in RMSE and 10% increase in R2. Also, the ANFIS–PSO model showed an approximately 53% decrease in RMSE and 9% increase in R2 compared to ANFIS. In other words, the ANFIS performance was optimized with the use of GA and PSO. 相似文献
5.
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
6.
对进化遗传算法进行了改进，提出了新的交叉算子和变异算子，使得改进后的算法具有更好的全局收敛能力。同时，引入与边坡稳定性密切相关的坡角、坡高、土体的抗剪强度等七个因子，建立了适用于边坡稳定性评价的多因素相关进化遗传算法边坡稳定性分析模型。实例应用结果表明，该算法应用于边坡的设计和稳定性评价具有较高的可信度。 相似文献
7.
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
8.
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
9.
Dig-limit optimization is an operational decision making problem that significantly affects the value of open-pit mining operations. Traditionally, dig-limits have been drawn by hand and can be defined as classifying practical ore and waste boundaries suiting equipment sizes in a bench. In this paper, an optimization approach based on a genetic algorithm (GA) was developed to approximate optimal dig-limits on a bench, given grade control data, equipment constraints, processing, and mining costs. A case study was conducted on a sample disseminated nickel bench, in a two destination and single ore-type deposit. The results from using the GA are compared to hand-drawn results. The study shows that GA-based approach can be effectively used for dig-limit optimization. 相似文献
10.
The use of spontaneous potential (SP) anomalies is well known in the geophysical literatures because of its effectiveness and significance in solving many complex problems in mineral exploration. The inverse problem of self-potential data interpretation is generally ill-posed and nonlinear. Methods based on derivative analysis usually fail to reach the optimal solution (global minimum) and trapped in a local minimum. A new simple heuristic solution to SP anomalies due to 2D inclined sheet of infinite horizontal length is investigated in this study to solve these problems. This method is based on utilizing whale optimization algorithm (WOA) as an effective heuristic solution to the inverse problem of self-potential field due to a 2D inclined sheet. In this context, the WOA was applied first to synthetic example, where the effect of the random noise was examined and the method revealed good results using proper MATLAB code. The technique was then applied on several real field profiles from different localities aiming to determine the parameters of mineralized zones or the associated shear zones. The inversion parameters revealed that WOA detected accurately the unknown parameters and showed a good validation when compared with the published inversion methods. 相似文献
11.
This paper presents a multi-level procedure for production and injection scheduling through a numerical model-based optimization of well control variables. To calculate the net present value (NPV), the objective function of optimization, this procedure uses a number of discretized systems for a reservoir model with different degrees of up-scaling prepared according to a multi-resolution wavelet technique. These up-scaled models were incorporated into optimization based on a probability function. In early optimization iterations, due to the necessity to explore the search space quickly, the coarsest grid model has a higher chance for selection than the others; however, by a selection (with a low probability) of the finest up-scaled grid model in these iterations, solutions and objective function were tuned. In the later iterations of optimization, the finest up-scaled grid model probability was the highest in order to ensure the reliability of the final solution. The optimization algorithm is an adaptive simulated annealing algorithm coupled with a polytope. This procedure was evaluated in two case studies. The first case study was a horizontal 2D oil model with water flooding. The second case study was a vertical 2D oil model with gas injection. The results show that the proposed optimization procedure provides approximately the same accuracy compared to the situation in which the fine grid model is used for all the optimization iterations. Also, the run-time for the proposed optimization procedure is comparable to the run-time of the optimization in which only the coarsest grid model is used to calculate objective function. Moreover, the superiority of the wavelet-based up-scaling over an analogous multiple grid system optimization using uniformly up-scaled models is presented. 相似文献
12.
Artificial neural networks (ANNs) have been extensively used for the spatially explicit modeling of complex geographic phenomena. However, because of the complexity of the computational process, there has been an inadequate investigation on the parameter configuration of neural networks. Most studies in the literature from GIScience rely on a trial-and-error approach to select the parameter setting for ANN-driven spatial models. Hyperparameter optimization provides support for selecting the optimal architectures of ANNs. Thus, in this study, we develop an automated hyperparameter selection approach to identify optimal neural networks for spatial modeling. Further, the use of hyperparameter optimization is challenging because hyperparameter space is often large and the associated computational demand is heavy. Therefore, we utilize high-performance computing to accelerate the model selection process. Furthermore, we involve spatial statistics approaches to improve the efficiency of hyperparameter optimization. The spatial model used in our case study is a land price evaluation model in Mecklenburg County, North Carolina, USA. Our results demonstrate that the automated selection approach improves the model-level performance compared with linear regression, and the high-performance computing and spatial statistics approaches are of great help for accelerating and enhancing the selection of optimal neural networks for spatial modeling. 相似文献
13.
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
14.
人口空间分布信息在环境健康风险诊断、自然灾害损失评估和现场抽样调查比较等地理学和相关学科研究中占有重要的地位。目前随着对地观测技术和地理信息科学的飞速发展, 如何精确地进行人口数据空间化成为了研究的难点和热点。针对采用传统方法解决人口空间化问题所遇到的困难和不足, 设计了遗传规划(genetic programming, GP)、遗传算法(genetic algorithms, GA) 和GIS 相结合的方法, 以GIS 确定量化影响因子权重, 以GP 建立模型结构, 以GA 优化模型参数, 成功建立研究区-山西省和顺县的人口数据格网分布表面。实验证明与传统建模方法(如逐步回归分析模型和重力模型)相比, 所提方法建模过程更为智能化与自动化, 模型结构更为灵活多样, 而且数据拟合精度更高。 相似文献
15.
Weights-of-evidence (WofE) and logistic regression techniques were used in a GIS framework to predict the spatial likelihood
(prospectivity) of crushed-stone aggregate quarry development. The joint conditional probability models, based on geology,
transportation network, and population density variables, were defined using quarry location and time of development data
for the New England States, North Carolina, and South Carolina, USA. The Quarry Operation models describe the distribution of active aggregate quarries, independent of the date of opening. The New Quarry models describe the distribution of aggregate quarries when they open. Because of the small number of new quarries developed
in the study areas during the last decade, independent New Quarry models have low parameter estimate reliability. The performance
of parameter estimates derived for Quarry Operation models, defined by a larger number of active quarries in the study areas,
were tested and evaluated to predict the spatial likelihood of new quarry development. Population density conditions at the
time of new quarry development were used to modify the population density variable in the Quarry Operation models to apply
to new quarry development sites. The Quarry Operation parameters derived for the New England study area, Carolina study area,
and the combined New England and Carolina study areas were all similar in magnitude and relative strength. The Quarry Operation
model parameters, using the modified population density variables, were found to be a good predictor of new quarry locations.
Both the aggregate industry and the land management community can use the model approach to target areas for more detailed
site evaluation for quarry location. The models can be revised easily to reflect actual or anticipated changes in transportation
and population features. 相似文献
16.
Strict control of the environmental impacts of blasting operations needs to be completely in line with the regulatory limits. In such operations, flyrock control is of high importance especially due to safety issues and the damages it may cause to infrastructures, properties as well as the people who live within and around the blasting site. Such control causes flyrock to be limited, hence significantly reducing the risk of damage. This paper serves two main objectives: risk assessment and prediction of flyrock. For these objectives, a fuzzy rock engineering system (FRES) framework was developed in this study. The proposed FRES was able to efficiently evaluate the parameters that affect flyrock, which facilitate decisions to be made under uncertainties. In this study, the risk level of flyrock was determined using 11 independent parameters, and the proposed FRES was capable of calculating the interactions among these parameters. According to the results, the overall risk of flyrock in the studied case (Ulu Tiram quarry, located in Malaysia) was medium to high. Hence, the use of controlled blasting method can be recommended in the site. In the next step, three optimization algorithms, namely genetic algorithm (GA), imperialist competitive algorithm (ICA) and particle swarm optimization (PSO), were used to predict flyrock, and it was found that the GA-based model was more accurate than the ICA- and PSO-based models. Accordingly, it is concluded that FRES is a very useful for both risk assessment and prediction of flyrock. 相似文献
17.
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.
This paper proposes a new approach to the mining exploration drillholes positioning problem (DPP) that incorporates both geostatistical and optimization techniques. A metaheuristic was developed to solve the DPP taking into account an uncertainty index that quantifies the reliability of the current interpretation of the mineral deposit. The uncertainty index was calculated from multiple deposit realizations obtained by truncated Gaussian simulations conditional to the available drillholes samplings. A linear programming model was defined to select the subset of future drillholes that maximizes coverage of the uncertainty. A Tabu Search algorithm was developed to solve large instances of this set partitioning problem. The proposed Tabu Search algorithm is shown to provide good quality solutions approaching 95% of the optimal solution in a reasonable computing time, allowing close to optimal coverage of uncertainty for a fixed investment in drilling. 相似文献
19.
In advanced exploration projects or operating mines, the process of allocating capital for infill drilling programs is a significant and recurrent challenge. Within a large company, the different mine sites and projects compete for the available funds for drilling. To maximize a project’s value to its company, a drillhole location optimizer can be used as an objective tool to compare drilling campaigns. The fast semi-greedy optimizer presented here can allow for the obtention of close to optimal solutions to the coverage problem with up to three orders of magnitude less computing time needed than with integer programming. The heuristic approach is flexible as it allows dynamic updating of block values once new drillholes are selected in the solution, as opposed to existing methods based on static block values. The block values used for optimization incorporate kriging estimate and variance, estimate of indicator at cutoff grade and distances to existing or newly selected drillholes. The heuristic approach tends to locate new drillholes within the maximum risk areas, i.e., within less informed zones predicted as being ore zones. Applied to different deposits, it enables, after suitable normalization, comparison of different drilling campaigns and allocation of budgets accordingly. 相似文献
20.
Natural habitats continue to dwindle due to a variety of natural and human-induced stressors. In response, sufficient land must be set aside for conservation to preserve long-term biodiversity. In this paper, we propose a bi-objective optimization model to form spatially cohesive nature reserves by minimizing the distance from habitat patches to the center of their reserve, while simultaneously maximizing the ecological condition of the patches set aside for preservation. Our model can accommodate multiple reserves which, combined with a minimum separation distance requirement, enforces backup coverage to mitigate the possible effects of natural and anthropogenic stressors. The model fully capitalizes on GIS functionalities to extract information on spatial relationships and visualize optimization results. Given the complexity of the separation constraint, we propose a genetic algorithm (GA) and a two-phase heuristic where the GA solves the selection of the reserves, while a solver attempts to optimize the allocation of patches to the selected reserves. The behavior of our model and its sensitivity to the parameters are illustrated on a simulated data set, while its real-world problem-solving capabilities are demonstrated on a case study in New Hampshire. Our model provides an alternative modeling tool for conservation planning, particularly when backup reserves are desired. 相似文献
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