一种自适应多目标离散差分进化算法

提出一种自适应多目标离散差分进化算法。该算法将差分进化引入多目标优化领域,采用一种新的自适应离散差分进化策略增强算法的全局搜索能力,以获得更优的Pareto近似解,并结合Pareto快速分层排序策略和基于聚集密度的按层修剪操作对种群进行更新维护,使解集保持良好的多样性。实例测试和算法比较表明,该算法能有效求解离散域和连续域上不同类型的多目标优化问题,且在收敛性、分布性、稳定性方面均表现较好。     

基于改进差分进化算法的PID优化设计

提出一种基于改进差分进化算法的PID控制器参数优化方法.针对差分进化算法的优化性能受控制参数取值和差分进化类型的影响较大,算法容易早熟收敛的问题,提出改进差分进化算法.该算法在标准差分进化理论基础上对差分矢量的初始种群、缩放因子、交叉概率和差分进化模式进行优化,将缩放因子和交叉概率由固定数值设计为随机函数,随着搜索过程的进行,自适应选取差分进化模式,从而增强搜索能力.在PID参数的优化设计中通过仿真实验研究,表明采用新方法获得的PID控制器性能优于基于常规方法、遗传算法和基本差分进化算法设计的PID控制器.     

基于差分进化的离散粒子群算法求解TSP问题

针对TSP问题,结合离散粒子群算法和差分进化算法各自的特点,提出了基于差分进化的离散粒子群算法。该算法先利用差分进化算法的变异、选择算子产生新的群体,再通过离散粒子群算法和交叉及选择算子进行局部搜索。通过对标准的30个城市进行实验,实验结果表明,该优化算法在求解TSP问题上有很好的性能。     

改进差分进化PID参数优化及在凝絮中的应用

针对标准差分进化（DE）收敛速度慢和操作过程中参数为常数从而导致算法鲁棒性相对较差的缺点,采用以个体适应度值最优的个体为基矢量,沿次优个体方向搜索的变异策略来提高算法的搜索速度;提出根据差分矢量大小和个体收敛情况自适应调整变异参数F和交叉概率CR的自适应参数调整策略来确保搜索初期种群的多样性和后期算法的局部搜索能力。并将基于该改进差分进化算法（MDE）优化后的PID控制器用于水厂加药凝絮过程的控制,仿真结果表明该算法收敛速度快,基于该算法的MDE-PID控制器性能好,是PID控制器参数整定的有效搜索方法,也是实现水厂加药凝絮过程良好控制的有效途径。     

一种粒子群和改进自适应差分进化混合算法及在生产调度中的应用

差分进化算法是一类基于种群的启发式全局搜索技术,但传统的差分进化算法存在停滞现象,容易使算法收敛停止。虽然之后出现了各种版本的自适应差分进化算法,但没有考虑到当代个体的适应值是否向着最优个体的适应值逼近,因此本文提出了一种新型的自适应差分进化算法FMDE。考虑到粒子群算法和差分进化算法类似,为了充分发挥两种算法的特点,提出了自适应差分进化和粒子群的混合算法PSO_FMDE;最后采用测试数据集对性能进行分析。实验结果表明,该算法根据进化过程中的搜索进度自适应地确定变异率,使算法易于跳出局部最优解,以提高全局搜索能力。PSO_FMDE算法较单一算法而言,性能更优,更易于靠近全局最优解。     

病毒进化的离散差分进化超声图像特征选择

选择具有识别作用的超声图像淋巴结区域特征对临床诊断具有重要价值。针对目前特征选择算法收敛速度慢和容易陷入局部极小值的问题,提出病毒协同进化的离散差分进化的颈部淋巴结超声图像特征选择算法。该算法主要利用病毒感染操作进行宿主个体的变异,在维持宿主个体多样性的同时保留最优的搜索信息,提高了算法的适应度函数值和进化速度。在临床颈部淋巴结超声图像中进行实验验证,分类精度达到98%,而算法平均收敛迭代次数仅为30次,表明本文所提算法是正确有效的。     

基于动态自适应策略的改进差分进化算法

针对差分进化算法处理复杂优化问题时存在后期收敛速度变慢、收敛精度不高和参数设置困难的问题,提出了一种基于动态自适应策略的改进差分进化算法(dn-DADE)。首先,新的变异策略DE/current-to-dnbest/1利用当前种群中的精英解引导有效的搜索方向来动态调整可选的精英解,使其在进化后期趋于全局最优解。其次,分别设计了缩放因子和交叉因子的自适应更新策略,使两者在搜索的不同阶段自适应变化,以弥补差分进化算法对参数敏感的不足,进一步提高算法的稳定性和鲁棒性。对14个benchmark函数进行了测试并与多种先进DE改进算法进行了比较,结果显示,dn-DADE算法具有较高的求解精度,收敛速度快,寻优性能显著。     

基于反向学习的自适应差分进化算法

为解决差分进化（DE）算法过早收敛与搜索能力低的问题,讨论对控制参数的动态调整,提出一种基于反向学习的自适应差分进化算法。该算法通过反向精英学习机制来增强种群的局部搜索能力,获取精确度更高的最优个体;同时,采用高斯分布随机性提高单个个体的开发能力,通过扩充种群的多样性,避免算法过早收敛,整体上平衡全局搜索与局部寻优的能力。采用CEC 2014中的6个测试函数进行仿真实验,并与其他差分进化算法进行对比,实验结果表明所提算法在收敛速度、收敛精度及可靠性上表现更优。     

多目标差分进化算法的电力系统无功优化

 在传统电力系统无功优化( Reactive Power Optimization,RPO) 模型中引入电压水平 指标,建立了以网损最小,电压水平最好为目标的多目标差分进化算法( Differential Evolution Algorithm) 的模型。针对基本差分进化算法易陷入局部最优解、收敛速度慢的缺点,提出一种 具有自适应参数策略的改进差分进化算法并首次用于多目标电力系统无功优化问题。通过在 算法进化过程中调整变异因子F 和交叉因子CR,在初期增加种群的多样性、扩大全局搜索区 域; 从而可以避免算法陷入局部最优解; 同时在后期也加快了收敛速度。将该算法用于电力系 统无功优化并仿真计算了IEEE-14 节点标准测试系统,结果验证模型和算法的有效性。     

基于复形法和云模型的差分进化混合算法

为了改善差分进化算法的收敛速度和优化精度, 提出一种基于复形法和云模型的差分进化混合算法（HDECC）。该算法使用差分进化算法搜索局部最优域, 引入复形法和云模型来加快算法的收敛速度和提高算法优化精度, 使算法的初期搜索速度和之后的优化精度得到相互平衡。最后, 使用七个标准约束优化问题和两个典型工程应用实例进行实验仿真, 实验结果表明, 与同类算法比较, HDECC算法全局搜索能力强、优化精度高、收敛速度快, 且算法更稳定。     

Tunnel-Free Supercover 3D Polygons and Polyhedra

A new discrete 3D line and 3D polygon, called Supercover 3D line and Supercover 3D polygon, are introduced. Analytical definitions are provided. The Supercover 3D polygon is a tunnel free plane segment defined by vertices and edges. An edge is a Supercover 3D line segment. Two different polygons can share a common edge and if they do, the union of both polygons is tunnel free. This definition of discrete polygons has the “most” properties in common with the continuous polygons. It is particularly interesting for modeling of discrete scenes, especially using tunnel-free discrete polyhedra. Algorithms for computing Supercover 3D Lines and Polygons are given and illustrated.     

Smoothing Normal Vectors on Discrete Surfaces While Preserving Slope Discontinuities

A new method is proposed which smoothes normal vectors over a discrete surface, preserving slope discontinuities and small details. Assume an estimate of the normal vector at each surface point is known and these estimates are computed from small neighbourhoods such that slope discontinuities and small details are still reflected by these normals. To smooth these normals, the normal vectors at points in a certain neighbourhood are averaged. The size of the neighbourhood considered for the smoothing at a point is adapted according the local surface configuration. The adaptation is performed, depending on the tangent plane at the point considered as well as the angles between the normals at neighbouring points and the normal at the point in question.     

离散非线性规划问题的改进遗传算法

针对实际离散非线性规划问题，分析了离散与连续变量优化问题和求解方法的不同及特性．根据离散变量与遗传算法的特点，将单纯形搜索与算术交叉思想相结合，提出离散单纯形交叉算子以提高遗传算法的局部寻优能力，将种群逐步向离散极值点进行引导，实现算法的快速离散寻优．同时，设计了离散变异算子，使遗传算子真正在离散空同中进行搜索．基于梯度下降思想提出离散修复算子，提高算法对非线性约束的处理能力．实际离散非线性规划问题的应用研究验证了方法的有效性．     

A computational comparison of several models for the exact solution of the capacity and distance constrained plant location problem

The Capacity and Distance Constrained Plant Location Problem is an extension of the discrete capacitated plant location problem, where the customers assigned to each plant have to be assigned to vehicles. In addition to the capacity constraints on the plants, there is a limit on the total distance traveled by each vehicle to serve its assigned customers by means of round trips. This paper presents several formulations for the problem and proposes different families of valid inequalities. The results of extensive computational experiments are presented and analyzed.     

Geometric segmentation of 3D scanned surfaces

The geometric segmentation of a discrete geometric model obtained by the scanning of real objects is affected by various problems that make the segmentation difficult to perform without uncertainties. Certain factors, such as point location noise (coming from the acquisition process) and the coarse representation of continuous surfaces due to triangular approximations, introduce ambiguity into the recognition process of the geometric shape. To overcome these problems, a new method for geometric point identification and surface segmentation is proposed.The point classification is based on a fuzzy parameterization using three shape indexes: the smoothness indicator, shape index and flatness index. A total of 11 fuzzy domain intervals have been identified and comprise sharp edges, defective zones and 10 different types of regular points. For each point of the discrete surface, the related membership functions are dynamically evaluated to be adapted to consider, point by point, those properties of the geometric model that affects uncertainty in point type attribution.The methodology has been verified in many test cases designed to represent critical conditions for any method in geometric recognition and has been compared with one of the most robust methods described in the related literature.     

Geometric properties estimation from discrete curves using discrete derivatives

Accurate geometric properties estimation from discrete curves is an important problem in many application domains, such as computer vision, pattern recognition, image processing, and geometric modeling. In this paper, we propose a novel method for estimating the geometric properties from discrete curves based on derivative estimation. We develop derivative estimation by defining the derivative of a discrete function at a point, which will be called the discrete derivative. Similarly, the second and higher order discrete derivatives at that point are also defined, and their convergence is demonstrated by theory analysis. These definitions of the different order discrete derivatives provide a simple and reliable way to estimate the derivatives from discrete curves. Based on the discrete derivatives, classical differential geometry can be discretized, and the geometric properties are estimated from discrete curves by using differential geometry theory. The proposed method is independent of any analytic curve and estimates the geometric properties directly from discrete data points, which makes it robust to the geometric shapes of discrete curves. Another advantage of the proposed method is the robustness to noise because of the calculation characteristics of the discrete derivatives. The proposed method is evaluated and compared with other existing methods in the experiments with both synthetic and real discrete curves. The test results show that the proposed method has good performance, and is robust to noise and suitable for different curve shapes.     

On the div-curl lemma in a Galerkin setting

Given a sequence of Galerkin spaces X _h of square-integrable vector fields, we state necessary and sufficient conditions on X _h under which it is true that for any two sequences of vector fields u _h ,u _h ′∈X _h converging weakly in L² and such that u _h is discrete divergence free and curl u _h ′ is precompact in H⁻¹, the scalar product u _h ⋅u′ _h converges in the sense of distributions to the right limit. The conditions are related to super-approximation and discrete compactness results for mixed finite elements, and are satisfied for Nédélec’s edge elements. We also provide examples of sequences of discrete divergence free edge element vector fields converging weakly to 0 in L² but whose divergence is not precompact in H _loc ⁻¹.      

ObjectARX环境下地图离散化软件的实现

利用AutoCAD下的二次开发工具ObjectARX，以城市数字矢量地图离散化为例，设计完成了NDS(Numerical Discrete Sys-tem)软件系统。并且分析、讨论了系统的实现方法，提出了相关技术的应用前景。     

Robust estimation of continuous nonlinear plants with discrete measurements

In this work the problem of designing a state estimator for completely or partially observable continuous nonlinear plants with discrete measurements is addressed. The combination of a geometric approach with a stability analysis yields an estimator design methodology with a nonlinear detectability condition susceptible of testing, a systematic estimator construction, a robust convergence criterion coupled with a simple tuning scheme, as well as a rationale to explain the interplay between sampling time, estimator gains, and estimator functioning. Comparing with the continuous measurement case where the convergence is attained by tuning the gain above a low limit, in the discrete measurement case the loss of information due to the measurement sampling increases the size of the lower gain limit, and imposes sampling time and high gain limits. The proposed methodology is applied to address the estimation problem of a class of solution homopolymerization reactors, and is tested with a methyl-methacrylate polymerization run taken from a previous extended Kalman filter implementation study with experimental data.