基于改进的遗传算法的多目标优化问题研究

研究多目标优化算法问题,针对传统的多目标优化算法由于计算复杂度非常高,难以获得令人满意的解等问题,在图论和遗传算法基础上,提出了一种改进的遗传算法求解多目标优化方法。首先采用二进制编码表示最小树问题,然后采用深度优先搜索算法进行图的连通性判断,给出了一种新的适应度函数,以提高算法执行速度和进化效率。最后仿真结果表明,与经典的Prim算法和Kruskal算法相比,新算法复杂度较低,并能在第一次遗传进化过程中获得一批最小生成树,适合于解决不同类型的多目标最小树问题。     

最小费用最大流问题的一种新算法

现有的最小费用最大流算法都有自身的缺陷,增广链的选取不当会给计算带来不便,同时费用也达不到理想的效果。鉴于对最小费用最大流算法的增广链选取和最小费用的探索,文章通过对费用差的定义给出了一种求最小费用最大流的新算法。新算法的原则是优先选择费用差最小的有向路径进行增广,当费用差相同时就选择修正后的路径。通过对最小费用最大流算法的改进,新算法易理解且便于计算。通过实例说明了新算法的有效性和执行效率。     

混沌梯度耦合优化算法及仿真研究

提出一种基于自适应混沌梯度下降的单目标耦合优化算法 .它采用变步长梯度下降法得到某个局部优化值 ,通过规则来判断其为局部极小值 ,然后利用一个由小到大变化的自适应尺度混沌遍历算法来获得一个更优值来代替局部极小值以跳出局部极小状态 ,全局优化值可以通过这种反复迭代来获得 .仿真结果表明 ,该算法能充分发挥梯度法寻优的快速性和混沌法寻优的全局搜索能力 ,有效地跳出局部极小 ,并快速找到最优值     

炼钢连铸生产中炉机优化匹配问题的算法

将炼钢连铸生产计划中炉机优化匹配问题归结为一个不允许等待的混合流水车间排序问题来进行研究,提出了一个启发式算法-最小偏差算法。通过实验设计,用大量随机数据进行了模拟和统计分析。结果表明,最小偏差算法是一种合理的、实用的、有效的算法。     

基于em算法且能以概率1全局收敛的混合学习算法

文章指出了随机神经网络em学习算法仍然存在着收敛于局部极小值之缺陷.针对三层随机感知机,文章将em学习算法与Solis和Wets的随机优化算法结合起来,提出了三层随机感知机的混合型新学习算法HRem.文章从理论的角度证明了混合型新学习算法HRem能以概率1全局收敛于随机感知机的基于Kullback-Leibler差异度量的最小值.这一理论结果对em学习算法的深入研究有重要意义.     

多重最小支持度频繁项集挖掘算法研究

某些情况下提取关联规则挖掘时需要根据项目的特点设置不同的最小支持度,针对此问题进行了多重最小支持度的频繁项集挖掘算法研究。在FP-growth的基础上提出了多重最小支持度树(MS-tree)的新方法,并设计了MS-growth算法对MS-tree进行频繁模式集的挖掘。该算法只需扫描一次数据库,克服了MSapriori算法在生成关联规则时需要重新扫描数据库的缺点。实验表明,新算法的性能可以和FP-growth算法相比,而且可以处理多重最小支持度的问题。     

混沌梯度组合优化算法

提出一种混沌梯度组合全局优化算法，并对该算法进行了收敛性分析．算法首先采用改进的变步长梯度法得到某个优化值，然后利用变尺度混沌搜索跳出局部极小，经过反复组合迭代，直至到达最优解．仿真结果表明，该算法能充分发挥梯度法寻优的快速性和混沌法寻优的全局搜索能力．     

Constructing a minimum height elimination tree of a tree in linear time

Given a graph, finding an optimal vertex ranking and constructing a minimum height elimination tree are two related problems. However, an optimal vertex ranking does not by itself provide enough information to construct an elimination tree of minimum height. On the other hand, an optimal vertex ranking can readily be found directly from an elimination tree of minimum height. On n-vertex trees, the optimal vertex ranking problem already has a linear-time algorithm in the literature. However, there is no linear-time algorithm for the problem of finding a minimum height elimination tree. A naive algorithm for this problem requires O(nlogn) time. In this paper, we propose a linear-time algorithm for constructing a minimum height elimination tree of a tree.     

基于最小面积差的三维模型简化算法

文章提出了一种基于面积误差度量下的三维网格模型简化方法。该方法通过极小化误差目标函数来简化三角网格模型。算法首先对边遍历,计算每条边的最小面积差;然后对面积差最小的边进行折叠;最后通过求解折叠边的最小面积差,确定新点的坐标。实验结果表明,该算法不仅可以反映局部表面几何变化,还可使模型仍具有较高保真度。最后用实例说明了该方法的有效性。     

A hybrid algorithm for finding a global minimum

One of the most important problems in non-linear programming is to find out the global minimum of a given objective function. In this paper, a new hybrid algorithm which combines the random optimization method of Matyas (1965) and one of the well-known ordinary descent algorithms having an effective convergence property (for example, the Fletcher-Reeves conjugate gradient method, the Davidon-Fletcher-Powell quasi Newton method, etc.) is proposed in order to find out a global minimum in as small a number of steps as possible. Several computational experiments on multimodal objective functions are carried out in order to test the efficiency of the proposed hybrid algorithm. The results obtained imply that the proposed hybrid algorithm is useful for finding out a global minimum in a small number of steps. A theorem that predicts convergence to a global minimum is also given.     

Genetic algorithms-based path-planning system for the motion of a group of mobile micro-robots

A genetic algorithm for planning optimal paths for a group of micro-robots in an environment with obstacles is considered. This algorithm allows one to solve the posed problem according to the criteria that are most frequently given, such as the minimum length of the path, minimum motion time, etc. The high performance of the proposed algorithm allows one to find a solution in real time, only using the resources of onboard computers of micro-robots. The simulation results and the optimal values of the parameters for which the algorithm is balanced in the best way are presented.     

A new learning algorithm with reference-following variables

A learning algorithm is presented for the learning of neural networks, in which the learning trajectory is convergence without any over-learning by changing of topological construction of the algorithm near any local minimum points of learning error. Became the topological construction is not convergent for some functions by usual BP method near some local minimum points, there is an over-learning phenomenon. To avoid the over-learning phenomenon, reference-foUowing variables are used to change the topological construction of this algorithm. The theoretical analysis and the simulation results indicate that the proposed method is simple and useful.     

A Newton-like optimization algorithm for digital systems with time delay

The design and computation problems of optimum nonlinear digital time-delay control systems is considered. A minimum principle is presented which solves the design problem, and a second-order computational algorithm is developed which solves the computation problem. The minimum principle is shown to be a special case of a generalized minimum principle in. Hilbert space. The computational algorithm is actually a Newton-like hill-climbing technique for iteratively improving the guessed initial values of the conjugate vector λ so as to satisfy the final condition of the problem. The main advantage of the present algorithm is that it does not require excess memory as in other algorithms.     

Sequential estimation in two-dimensional discrete random fields

A sequential minimum variance estimation algorithm for a discrete two-dimensional random field modeled by a non-symmetric half-plane (NSHP) model is derived. A modification of the algorithm to facilitate inplace computation is presented and its implementation discussed. The application of the algorithm in image processing is illustrated by using the algorithm to restore a class of monochromatic images modeled by a symmetric (1,) NSHP model.     

一种基于改进堆优化Dijkstra算法的最小费用最大流算法

通过最短路径算法在残存网络中搜索汇点的最小费用路径是流网络中求解最小费用最大流的主要方式,而Dijkstra算法是最高效的最短路径算法之一。本文通过证明残存网络中不存在负循环,采用改进的堆优化Dijkstra算法在残存网络中搜索最小费用路径以提升算法的效率。实验结果表明,与经典的基于最短路径快速算法的最小费用最大流算法和基于Bellman-Ford算法的最小费用最大流算法对比,本文提出的改进算法具有更高的时间效率。     

面向VLSI实现的FFT并行算法

本文提出了一种新的面向ＶＬＳＩ实现的ＦＦＴ并行算法，其中旋转因子所占ＲＯＭ的存储容量达到最小，因而有利于ＦＦＴ处理器的片内集成。     

A heuristic procedure to minimize number of tardy jobs and total tardiness in single machine scheduling

Moore has suggested an optimizing algorithm for minimizing the number of tardy jobs in single machine scheduling. This article explores the possibility of modifying the Moore's algorithm so that total tardiness is minimized while keeping the number of tardy jobs minimum. A heuristic procedure based on the Moore's original algorithm is presented and its performance is evaluated. A simple extension of the algorithm to parallel machine scheduling is also provided.     

Hybridization of gradient descent algorithms with dynamic tunnelingmethods for global optimization

An algorithm based on gradient descent techniques with dynamic tunneling methods fur global optimization is proposed. The proposed algorithm consists of gradient descent for local search and a direct search scheme, based on dynamic tunneling technique, for repelling away from local minimum to find the point of next local descent. This search process applied repeatedly finds the global minimum of an objective function. The convergence properties of the proposed algorithm is validated experimentally on benchmark problems. A comparative computational results confirm the importance of dynamic tunneling in gradient descent techniques     

具有一般交互矩阵的多变量系统的随机直接自适应控制

本文使用系统的交互矩阵,提出了基于广义最小方差控制律的一般随机多变量系统的直 接自适应控制算法,并对该算法进行了稳定性和收敛性分析.该算法即使用于非最小相位系 统仍然具有全局收敛特性.     

最小k-击中集问题的一个随机近似算法

Franco等给出一个基于平均度数的最小3-击中集问题的贪心算法,并给出算法返回击中集规模的上界.首先将其算法推广成为求解最小k-击中集问题的贪心算法HGREEDY1(A,C),并类似地给出算法返回击中集规模的上界;然后给出基于最大度数的贪心算法HGREEDY2(A,C),并证明算法HGREEDY2(A,C)在给定条件下返回的击中集规模上界优于算法HGREEDY1(A,C);另外设计了用于求解最小k-击中集的随机算法RH(A,C),并对其性能进行平均分析;在此基础上设计一个求解最小k-击中集的随机近似算法并讨论其性质.