基于蚁群算法的道路选取模型研究

在制图综合中,道路选取是非常重要的内容之一,研究道路选取的智能化方法是非常必要的。文中在研究道路语义、几何特征、拓扑关系和结构特征的基础上,充分考虑居民点对道路选取过程的影响,建立道路选取的蚁群算法模型。通过实验证明该模型算法的可行性和有效性。     

基于交叉点的道路曲线化简算法研究

现有的曲线化简算法不能很好地化简具有交叉路口的道路曲线,针对这一问题提出一种基于交叉点的道路曲线化简算法。算法分为预化简和修正化简两个阶段:首先识别并得到曲线上的分段点,利用相邻的分段点作为道格拉斯-普克算法的首尾点对曲线进行化简,得到预化简的结果;然后对于交叉点引入偏差阈值ε,通过判断道路曲线交叉点与化简后交叉点的距离与偏差精度ε的大小关系来确定该交叉点的化简与保留,如果保留或者化简后的道路曲线没有交叉点那么将原交叉点作为分段点对此段曲线进行重新化简。理论分析与实验结果表明,文中算法能够有针对性地保留或化简道路交叉点以及保持曲线化简后的形态特征。     

基于CLEAN算法的GPS监测数据处理方法

对建筑物进行高精度的变形监测是后期信息提取,预防安全隐患的基础。针对GPS变形监测中存在的多径误差问题,本文提出一种基于CLEAN算法的GPS多径提取方法,首先对多径信号理论模型进行分析,结果表明多径信号可以作为直达信号的谐波分量,在此基础上利用CLEAN算法在频域对多径信号进行提取,基于仿真和实测数据的实验结果表明,所提方法相对于小波方法和经验模态分解(Empirical Mode Decomposition,EMD)方法具有更高的提取精度和更好的多径抑制性能。     

陆面温度反演算法——劈窗算法的敏感度分析

首先给出ＣＯ２ 倍增下遥感光合作物产量的概念模型,之后分析未受ＣＯ２ 倍增的遥感光合作物产量估测模型;在考虑ＣＯ２ 倍增对作物产量的影响后,对影响干物质累积的作物光合速率的模型进行修正,进而修正遥感光合作物产量估测模型。建立ＣＯ２ 倍增下作物产量响应模型,求取各参数,并在ＣＯ２ 倍增下对我国华北地区冬小麦产量响应进行填图,表明模型的估测结果有良好的可比性。     

不确定性选址问题探讨

阐述了GIS网络分析中不确定性选址问题的基本模型及特性。从问题的定义可知其为NP完备类问题。推导了最优解在紧条件的下界算法,并结合广义Powell算法及遗传算法,提出了不确定性选址问题的混合遗传算法,实验证明,在最优解的品质和收敛速度上都达到了比较好的效果。同时,实验的结果从另一个角度证明,如果兼顾收敛速度和解的品质这两个指标,单纯的遗传算法未必比其他搜索算法更优越,采用一些局部搜索性能较好的算法结合遗传算法,可以从两方面改善求解效果。     

无拓扑矢量数据快速压缩算法的研究与实现

首先论述传统矢量数据压缩算法：道格拉斯—普克法，分析将其应用于多边形边界数据压缩所造成的图形失真现象，在此基础上提出一种针对无拓扑矢量数据的快速压缩算法，并在MapInfo环境中实现该算法。     

The Integration of Cooperation Model and Genetic Algorithm

In the photogrammetry,some researchers have applied genetic algorithms in aerial image texture classification and reducing hyper-spectrum remote sensing data.Genetic algorithm can rapidly find the solutions which are close to the optimal solution.But it is not easy to find the optimal solution.In order to solve the problem,a cooperative evolution idea integrating genetic algorithm and ant colony algorithm is presented in this paper.On the basis of the advantages of ant colony algorithm,this paper proposes the method integrating genetic algorithms and ant colony algorithm to overcome the drawback of genetic algorithms.Moreover,the paper takes designing texture classification masks of aerial images as an example to illustrate the integration theory and procedures.     

改进Moser法射线追踪

地震波场正演模拟、层析成像、偏移成像经常需要用到射线追踪,本文在基于图形理论的Ｍｏｓｅｒ法基础上,改进其网格节点的划分、追踪时路径的选取,并增加直射线追踪以消除在速度变化不大时射线受节点布置影响出现不合理的折曲现象．     

基于遗传算法的巷道位移反分析研究

将遗传算法应用到位移反分析研究中,采用遗传算法与FLAC数值软件相结合的手段,研制了弹塑性巷道位移反分析程序。对该程序测试的结果表明,用遗传算法这一模拟生物进化的方法,进行巷道位移反分析,可以同时反演E,μ,C,ф,σx,σy,τxy7个参数,反演精度较高。     

Improving the efficiency of ant algorithms using adaptive refinement: Application to storm water network design

The ant algorithm is a new evolutionary optimization method proposed for the solution of discrete combinatorial optimization problems. Many engineering optimization problems involve decision variables of continuous nature. Application of the ant algorithm to the optimization of these continuous problems requires discretization of the continuous search space, thereby reducing the underlying continuous problem to a discrete optimization problem. The level of discretization of the continuous search space, however, could present some problems. Generally, coarse discretization of the continuous design variables could adversely affect the quality of the final solution while finer discretization would enlarge the scale of the problem leading to higher computation cost and, occasionally, to low quality solutions. An adaptive refinement procedure is introduced in this paper as a remedy for the problem just outlined. The method is based on the idea of limiting the originally wide search space to a smaller one once a locally converged solution is obtained. The smaller search space is designed to contain the locally optimum solution at its center. The resulting search space is discretized and a completely new search is conducted to find a better solution. The procedure is continued until no improvement can be made by further refinement. The method is applied to a benchmark problem in storm water network design discipline and the results are compared with those of existing methods. The method is shown to be very effective and efficient regarding the optimality of the solution, and the convergence characteristics of the resulting ant algorithm. Furthermore, the method proves itself capable of finding an optimal, or near-optimal solution, independent of the discretization level and the size of the colony used.