基于蚁群优化的MPPT算法研究

为了提高光伏发电系统最大功率点跟踪效果,提出了一种蚁群优化算法,算法通过迭代法更新占空比来趋近光伏电池的最大功率点.利用MATLAB软件对光伏电池进行建模与仿真,仿真结果验证了算法的可行性,并说明了算法能够快速地跟踪最大功率点.     

基于改进ICP算法的曲面特征配准研究

随着3D激光扫描技术的发展,点云数据的应用越来越广泛.然而点云配准一直是点云数据预处理过程中的一个关键问题.目前ICP算法是实现点云配准的主流算法.然而面对数据量大、噪声大的点云数据时,ICP算法在执行的配准效率和执行效果上不够理想.本文通过PCA算法,提取了点云数据集的方向向量,根据源数据与目标数据的方向向量,初步设定了旋转矩阵R的值.此外,定义了源数据与目标数据的曲面距离,在此基础上改进了传统的ICP算法.将改进后的ICP算法成功的应用到点云数据配准中来,提高了点云数据的配准效果,并压缩了算法的执行时间.     

基于核函数求解线性互补问题的不可行内点算法

本文研究了线性互补问题内点算法.利用全牛顿步长求解迭代方向,获得了算法迭代复杂性为O(nlogn/ε),推广了Roos等关于线性规划问题不可行内点算法,其复杂性与目前最好的不可行内点算法复杂性一致.     

一种求解不等式约束优化问题的光滑化算法

利用光滑函数建立了不等式约束优化问题KT条件的一个扰动方程组,提出了一个新的内点型算法.该算法在有限步终止时当前迭代点即为优化问题的一个精确稳定点.在一定条件下算法具有全局收敛性,数值试验表明该算法是有效的.     

点到集映像和点到集映像族的连续化

一个最优化算法的迭代过程,可以看做一个点到集映像的取值过程。从这个观念出发,Zangwill建立了全局收敛定理,统一了许多算法的收敛性证明。但是,同一个算法可以看做许多不同的点到集映像的取值过程。事实上,把一点对应于从该点按算法可能达到的下一点的全体形成的点到集映像称为算法所对应的点到集映像,那么,任一点到集映像,若它的像集于任一点外都包含算法所对应的点到集映像的像集,其取值过程均包含了     

基于指数型核函数的线性规划原始对偶内点算法

给出线性规划原始对偶内点算法的一个单变量指数型核函数.首先研究了这个指数型核函数的性质以及其对应的障碍函数.其次,基于这个指数型核函数,设计了求解线性规划问题的原始对偶内点算法,得到了目前小步算法最好的理论迭代界.最后,通过数值算例比较了基于指数型核函数的原始对偶内点算法和基于对数型核函数的原始对偶内点算法的计算效果.     

对称线性互补问题的乘性Schwarz算法

本文提出了求解对称性互补问题的乘性Schwarz算法,其中子问题用投影迭代方法求解.利用投影迭代算子的性质及投影迭代的收敛性,证明了算法产生的迭代点列的聚点为原互补问题的解,并在一定条件下,证明算法产生的迭代点列的聚点存在.     

一个求解线性不等式约束的非线性规划的广义梯度投影内点算法

基于内点算法思想,利用广义投影技术设计了求解带线性不等式约束和非负约束的非线性规划的广义梯度投影内点算法,并讨论了算法的收敛性质,数值例子表明算法是有效的.     

关于DC函数最小化邻近点算法的注记

本文证明DC函数最小化问题邻近点算法的一个收敛性定理,并对此问题提出一类非精确邻近点算法．     

单调非线性互补问题的不可行内点算法

为了克服内点算法初始点不易给出的缺陷，本文给出了一个求解单调非线性互补问题的不可行内点算法，并证明了算法的收敛性。     

负权最短路问题的新算法

Bellman-Ford算法自1958年以来一直是负权最短路问题的公认的最好算法之一．1970年,Yen对其进行了改进,理论上可以节省一半的计算量．本文得到了一种比Bellman-Ford算法更加优越的算法．尽管在理论上新算法无法保证完全超越于Yen的改进算法,但在许多情况下需要更少的计算量．     

A flexible CMRH algorithm for nonsymmetric linear systems

A flexible version of the CMRH algorithm is presented that allows varying preconditioning at every step of the algorithm. A consequence of the flexibility of this new variant is that any iterative methods can be incorporated as a preconditioner in the inner steps. Theoretical results that relate the residual norm of the new algorithm and the flexible GMRES, the new algorithm with CMRH itself, are given. Numerical experiments are carried out to illustrate the effectiveness of the proposed algorithm in comparison with the standard CMRH algorithm, ILU-preconditioned CMRH variants and the flexible GMRES algorithm.     

Implicitly restarted projection algorithm for solving optimization problems

An algorithm for solving the problem of minimizing a quadratic function subject to ellipsoidal constraints is introduced. This algorithm is based on the impHcitly restarted Lanczos method to construct a basis for the Krylov subspace in conjunction with a model trust region strategy to choose the step. The trial step is computed on the small dimensional subspace that lies inside the trust region.

One of the main advantages of this algorithm is the way that the Krylov subspace is terminated. We introduce a terminationcondition that allows the gradient to be decreased on that subspace.

A convergence theory for this algorithm is presented. It is shown that this algorithm is globally convergent and it shouldcope quite well with large scale minimization problems. This theory is sufficiently general that it holds for any algorithm that projects the problem on a lower dimensional subspace.     

基于固定序的Bellman-Ford算法的改进

固定序算法是Bellman-Ford算法的一种基本改进算法。为了改变固定序算法在稀疏图上的劣势,本文通过预先订制参与迭代的点的计算顺序,对该算法进行了改进。实验表明,在稀疏图上, 改进后的算法相对于原算法计算效率提高了近50%, 并能够与国际流行的先进先出算法相媲美。本文的工作表明,固定序算法不仅在大规模稠密图上具有明显的优势,而且在稀疏图上也具有很强的竞争力。     

基于变分不等式的支持向量机优化算法研究

由于标准支持向量机模型是一个二次规划问题,随着数据规模的增大,求解算法过程会越来越复杂.在K-SVCR算法结构的基础上,构造了严格凸的二次规划新模型,该模型的主要特点是可以将其一阶最优化条件转化为变分不等式问题,利用Fischer-Burmeister(FB)函数将互补问题转化为光滑方程组;建立光滑快速牛顿算法求解,并证明了该算法所产生的序列是全局收敛;利用标准数据集测试提出算法的有效性,在训练正确率和运行时间上与K-SVCR算法相比都有较好的表现,实验结果表明该算法可行且有效.     

A descent algorithm for nonsmooth convex optimization

This paper presents a new descent algorithm for minimizing a convex function which is not necessarily differentiable. The algorithm can be implemented and may be considered a modification of the ε-subgradient algorithm and Lemarechal's descent algorithm. Also our algorithm is seen to be closely related to the proximal point algorithm applied to convex minimization problems. A convergence theorem for the algorithm is established under the assumption that the objective function is bounded from below. Limited computational experience with the algorithm is also reported.     

惯性算法的收敛性和稳定性

1．引言对于非线性发展方程，人们感兴趣的是解的渐近行为．当某一物理参数人很小时，非定常解趋向定常解，而当入充分大时，非定常解的渐近行为完全表现在一个吸引子的结构上，这个吸引子可能是具有分数维数的分形结构．在试图逼近这个吸引子的设想当中，惯性流形显示了它的巨大优越性［1－4］．一个系统的惯性流形是一个光滑的有限维流形，它以指数级速度逼近吸引子．在这个光滑的流形上，一个偏微系统可以用它的惯性形式即有限维常微系统来得到．然而在目前状况下，人们知道存在惯性流形的非线性发展方程为数不多．而绝大部分非线性发展…     

An adaptive membrane algorithm for solving combinatorial optimization problems

Membrane algorithms (MAs), which inherit from P systems, constitute a new parallel and distribute framework for approximate computation. In the paper, a membrane algorithm is proposed with the improvement that the involved parameters can be adaptively chosen. In the algorithm, some membranes can evolve dynamically during the computing process to specify the values of the requested parameters. The new algorithm is tested on a well-known combinatorial optimization problem, the travelling salesman problem. The empirical evidence suggests that the proposed approach is efficient and reliable when dealing with 11 benchmark instances, particularly obtaining the best of the known solutions in eight instances. Compared with the genetic algorithm, simulated annealing algorithm, neural network and a fine-tuned non-adaptive membrane algorithm, our algorithm performs better than them. In practice, to design the airline network that minimize the total routing cost on the CAB data with twenty-five US cities, we can quickly obtain high quality solutions using our algorithm.     

AN ADAPTIVE MEMBRANE ALGORITHM FOR SOLVING COMBINATORIAL OPTIMIZATION PROBLEMS

Membrane algorithms （MAs）, which inherit from P systems, constitute a new parallel and distribute framework for approximate computation. In the paper, a membrane algorithm is proposed with the improvement that the involved parameters can be adaptively chosen. In the algorithm, some membranes can evolve dynamically during the computing process to specify the values of the requested parameters. The new algorithm is tested on a well-known combinatorial optimization problem, the travelling salesman problem. The em-pirical evidence suggests that the proposed approach is efficient and reliable when dealing with 11 benchmark instances, particularly obtaining the best of the known solutions in eight instances. Compared with the genetic algorithm, simulated annealing algorithm, neural net-work and a fine-tuned non-adaptive membrane algorithm, our algorithm performs better than them. In practice, to design the airline network that minimize the total routing cost on the CAB data with twenty-five US cities, we can quickly obtain high quality solutions using our algorithm.     

An Application of a Multi-Objective Tabu Search Algorithm to a Bicriteria Flowshop Problem

This paper proposes a new tabu search algorithm for multi-objective combinatorial problems with the goal of obtaining a good approximation of the Pareto-optimal or efficient solutions. The algorithm works with several paths of solutions in parallel, each with its own tabu list, and the Pareto dominance concept is used to select solutions from the neighborhoods. In this way we obtain at each step a set of local nondominated points. The dispersion of points is achieved by a clustering procedure that groups together close points of this set and then selects the centroids of the clusters as search directions. A nice feature of this multi-objective algorithm is that it introduces only one additional parameter, namely, the number of paths. The algorithm is applied to the permutation flowshop scheduling problem in order to minimize the criteria of makespan and maximum tardiness. For instances involving two machines, the performance of the algorithm is tested against a Branch-and-Bound algorithm proposed in the literature, and for more than two machines it is compared with that of a tabu search algorithm and a genetic local search algorithm, both from the literature. Computational results show that the heuristic yields a better approximation than these algorithms.