一类复杂生产调度问题的基于符号演绎的调度方法

针对带有时问不确定件的复杂生产过程调度问题,提出一种基于符号演绎的调度方法.首先将时间的不确定性信息看作符号型数据,并提出一种用于处理这些符号型数据的基于不确定区间的符号演绎方法;然后将此符号演绎方法与遗传算法相结合,提出一种预排调度计划与实时调度规则相结合的调度方法来求解上述复杂生产调度问题.实验表明,将基于符号演绎的调度方法用于求解带有时间不确定性的复杂生产过程调度问题,能够取得较好的调度效果.     

A Simplex-based simulated annealing algorithm for node-arc capacitated multicommodity network design

The network design is a well-known problem, both of practical and theoretical significance. Network design models are extensively used to represent a wide range of planning and operations management issues in transportation, telecommunications, logistics, production and distribution. This paper presents a solution method for node-arc formulation of capacitated fixed-charge multicommodity network design problems. The proposed method is a hybrid algorithm of Simplex method and simulated annealing metaheuristic. The basic idea of the proposed algorithm is to use a simulated annealing algorithm to explore the solution space, where the revised Simplex method is used to evaluate, select and implement the moves. In the proposed algorithm, the neighborhood structure is pivoting rules of the Simplex method that provide an efficient way to reach the neighbors of current solution. To evaluate the proposed algorithm, the standard problems with different sizes are used. The algorithm parameters are tuned by design of experiments approach and the most appropriate values for the parameters are adjusted. The performance of the proposed algorithm is evaluated by statistical analysis. The results show high efficiency and effectiveness of the proposed algorithm.     

基于多机调度问题的动态规划算法

动态规划设计策略对许多具有最优解的实际应用问题的解决是灵活和有效的。文中首先针对在多机系统的操作系统的一类多机调度问题进行了分析,并给出了该类问题的动态规划算法,最后对所给算法的复杂度进行了分析和讨论。实验结果验证了所提出方法的有效性。     

Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations

Hybridizing of the optimization algorithms provides a scope to improve the searching abilities of the resulting method. The purpose of this paper is to develop a novel hybrid optimization algorithm entitled hybrid robust differential evolution (HRDE) by adding positive properties of the Taguchi's method to the differential evolution algorithm for minimizing the production cost associated with multi-pass turning problems. The proposed optimization approach is applied to two case studies for multi-pass turning operations to illustrate the effectiveness and robustness of the proposed algorithm in machining operations. The results reveal that the proposed hybrid algorithm is more effective than particle swarm optimization algorithm, immune algorithm, hybrid harmony search algorithm, hybrid genetic algorithm, scatter search algorithm, genetic algorithm and integration of simulated annealing and Hooke-Jeevespatter search.     

病态线性系统的控制模型设计及其迭代算法

建立一种解决病态线性系统的控制模型，得到求解病态线性系统近似解的一种新算法．该算法为病态线性系统利用计算机迭代求解提供了直接的算法依据，同时也适合于常态线性系统近似解的计算．最后将新算法与传统的Gauss—Seidel算法进行比较．     

一种基于逻辑栅格的网格自动生成算法

针对基于栅格法对表面变动较大的模型进行网格划分适应性差的问题,提出一种基于逻辑栅格的六面体网格自动生成算法。该算法对基于栅格法的思想进行改进,避开一次生成模型初始栅格,将模型离散为一系列截面,对相邻截面采用矢量求交方法形成一对截面的初始栅格,修正初始栅格形成两截面之间的一层网格,形成整体模型。采用改进的栅格法进行复杂地形拱坝坝肩块体网格自动生成,取得较好的效果,结果证明改进的栅格法对复杂地形适应性强,模型表面变动较大时能自动进行网格退化。     

一种基于速度的移动对象轨迹简化算法

目前对移动对象轨迹简化问题分为离线简化和在线简化。以往的简化方法中许多依赖轨迹的几何特性,而针对移动对象的速度这一重要特征没有足够的关注。本文基于速度研究移动对象轨迹的离线简化新方法,提出了移动对象轨迹离线简化的动态规划算法、基于广度优先搜索的轨迹简化算法及其优化算法、时间复杂度更低的近似算法。并通过大量实验验证本文提出的算法比基于方向的简化算法和基于位置的简化算法具有更好的简化效率。     

嵌入单纯形法的混合类电磁机制算法

针对类电磁机制算法存在局部搜索能力差的问题,提出一种基于单纯形法的混合类电磁机制算法。该混合算法首先利用反向学习策略构造初始种群以保证粒子均匀分布在搜索空间中。利用单纯形法对最优粒子进行局部搜索,增强了算法在最优点附近的局部搜索能力,以加快算法的收敛速度。四个基准测试函数的仿真实验结果表明,该算法具有更好的寻优性能。     

Network reconfiguration of unbalanced distribution networks using fuzzy-firefly algorithm

This paper addresses a method to optimize the unbalanced distribution networks (UDNs) for keeping up the voltage profile with respect to the consequence of solving the multi-objective reconfiguration using the Firefly algorithm in a fuzzy domain with a load flow method proposed in this paper. The objectives to be minimized are the total network power losses, the deviation of bus voltage and load equalizing in the feeders with network reconfiguration. Every goal is moved into the fuzzy domain utilizing its membership function and fuzzified independently. The proposed method for network reconfiguration has been implemented in 25-node and 19-node UDNs. The outcomes obtained by the suggested method of these two unbalanced networks have been compared with that of obtained by Genetic algorithm (GA), ABC algorithm, PSO algorithm and GA-PSO algorithm using the same objective function. The juxtaposition of the proposed method with the available methods is also presented.     

放射形配网潮流计算的一种新的牛顿法

前推回推法是放射形配网潮流计算最基本的算法.通过对前推回推法求解过程的数学演化,导出一种新的牛顿类型的算法及其雅可比矩阵直接分解公式.利用比较原理,间接证明该算法是一种具有超线性收敛性的近似牛顿法.与经典牛顿法相比,该算法无须计算雅可比矩阵、无须三角因子分解等过程,直接由前代/回代或回代/前代过程就能完成;与前推回推法相比,该算法无须特定的节点和支路编号过程.文中以一个实际的中等规模配电系统为例,分析、比较前推回推法、导出的近似牛顿法、经典牛顿法等的收敛性和计算速度,证实上述研究结论.     

Fast artificial bee colony and its application to stereo correspondence

Nature-inspired meta-heuristics have gained popularity for solutions to many real-world complex problems, and the artificial bee colony algorithm is one of the most powerful optimisation methods among meta-heuristics. However, inefficient exploitation of onlooker bees prevents the artificial bee colony algorithm from finding the final result accurately and efficiently for complex problems. In this paper, a novel optimisation method is proposed based on the artificial bee colony algorithm. The proposed optimisation method adaptively exploits onlooker bees over generations. In addition, the proposed optimisation method is applied to a stereo-matching problem to minimise the segment-based integer energy function, which is also introduced in this paper. The experimental results show that the proposed optimisation method outperforms state-of-the-art population-based meta-heuristics, such as the genetic algorithm, differential evolution, conventional artificial bee colony, and clonal selection algorithm, for benchmark functions as well as for the stereo-matching problem.     

多率系统Kalman滤波算法的鲁棒性分析

多率系统Kalman滤波算法是多率采样系统中对多源观测进行融合的重要手段. 基于化工过程的多率采样特点, 给出了多率Kalman滤波算法, 分析了该算法在模型失配情况下的鲁棒性. 在给定的假设条件下, 通过对滤波误差变化规律的分析, 给出了多率Kalman滤波稳定与发散的基本判定方法. 针对一类典型系统, 推导出了滤波稳定与发散判据. 通过仿真对该判据进行了验证, 仿真结果表明所提出的滤波鲁棒性分析方法可以用于算法的实际应用.     

基于网格最小生成树的聚类算法选择

为得到好的聚类效果,需要挑选适合数据集簇结构的聚类算法。文中提出基于网格最小生成树的聚类算法选择方法,为给定数据集自动选择适合的聚类算法。该方法首先在数据集上构建出网格最小生成树,由树的数目确定数据集的潜在簇结构,然后为数据集选择适合所发现簇结构的聚类算法。实验结果表明该方法较有效,能为给定数据集找出适合其潜在簇结构的聚类算法。     

Fast segmentation of range images into planar regions by scan line grouping

A novel technique is presented for rapid partitioning of surfaces in range images into planar patches. The method extends and improves Pavlidis' algorithm (1976), proposed for segmenting images from electron microscopes. The new method is based on region growing where the segmentation primitives are scan line grouping features instead of individual pixels. We use a noise variance estimation to automatically set thresholds so that the algorithm can adapt to the noise conditions of different range images. The proposed algorithm has been tested on real range images acquired by two different range sensors. Experimental results show that the proposed algorithm is fast and robust.     

面向算法的SIMD计算机数学模型及其应用研究

针对数据并行计算在图像处理中的应用研究,提出了数据并行计算机的面向算法的数学模型,以及利用该模型得到的一种新颖的、数据并行算法的数学描述方法．采用该数学描述方法对数据并行图像处理中的灰度直方图运算、区域增长法图像分割以及图像卷积运算等3类图像处理方法进行了描述．结果表明,该数学描述方法不仅简单可行和精确,而且,可以从数学公式中直接得到算法的通信复杂性和计算复杂性．该方法可以应用到数据并行计算的应用研究中作为数学描述的工具．     

基于K-means聚类的数字半色调算法

数字半色调是在二值设备或多色二值设备上实现图像再现的一门技术,提出将K-means聚类法应用在数字半色调技术中。算法中应用人类视觉系统模型(HVS)和印刷模型最大限度减少原始灰度连续调图像和半色调图像之间的视觉误差;利用K-means聚类法将灰度图像划分成聚类分区,在每个聚类分区应用最小平方法(least-squares)最小化二值半色调图像和原始灰度级图像之间的平方误差,所构造的半色调算法与基于模型的最小平方法(LSMB)算法相比,随着聚类分区的增加,图像平滑且边缘清晰度增加,尤其是在图像细节部位。与LSMB算法比较,该算法的均方误差值有所降低,而权重信噪比和峰值信噪比提高了0.2～2 dB,模拟实验结果验证了算法的有效性。     

Chance-constrained target tracking using sensors with bounded fan-shaped sensing regions

We present a robust target tracking algorithm for a mobile robot. It is assumed that a mobile robot carries a sensor with a fan-shaped field of view and finite sensing range. The goal of the proposed tracking algorithm is to minimize the probability of losing a target. If the distribution of the next position of a moving target is available as a Gaussian distribution from a motion prediction algorithm, the proposed algorithm can guarantee the tracking success probability. In addition, the proposed method minimizes the moving distance of the mobile robot based on the chosen bound on the tracking success probability. While the considered problem is a non-convex optimization problem, we derive a closed-form solution when the heading is fixed and develop a real-time algorithm for solving the considered target tracking problem. We also present a robust target tracking algorithm for aerial robots in 3D. The performance of the proposed method is evaluated extensively in simulation. The proposed algorithm has been successful applied in field experiments using Pioneer mobile robot with a Microsoft Kinect sensor for following a pedestrian.     

传递函数阵子子模型参数递推估计：辅助模型方法

本文提出了递推估计传递函数阵子子模型(SSM)参数的辅助模型方法。不同噪信比下的数字仿真结果表明了所提出的算法的有效性。该算法计算量小,且可用于在线辨识。     

一种基于等效线性化的非平稳随机动力响应分析的显式迭代算法

提出了一种基于等效线性化法的非平稳随机动力响应分析的显式迭代算法.首先根据等效线性化法把非线性系统转化为离散的线性系统,然后应用Newmark-β积分方法,推导出各个离散时刻的时域显式迭代公式,进而可以快速得到非线性系统的随机动力响应,最后用一个非线性的范德波尔系统和一个杜芬系统受非平稳随机荷载的算例验证了该算法的计算精度和计算效率.     

An improved krill herd algorithm with global exploration capability for solving numerical function optimization problems and its application to data clustering

Krill herd algorithm is a stochastic nature-inspired algorithm for solving optimization problems. The performance of krill herd algorithm is degraded by poor exploitation capability. In this study, we propose an improved krill herd algorithm (IKH) by making the krill the global search capability. The enhancement comprises of adding global search operator for exploration around the defined search region and thus the krill individuals move towards the global best solution. The elitism strategy is also applied to maintain the best krill during the krill update steps. The proposed method is tested on a set of twenty six well-known benchmark functions and is compared with thirteen popular optimization algorithms, including original KH algorithm. The experimental results show that the proposed method produced very accurate results than KH and other compared algorithms and is more robust. In addition, the proposed method has high convergence rate. The high performance of the proposed algorithm is then employed for data clustering problems and is tested using six real datasets available from UCI machine learning laboratory. The experimental results thus show that the proposed algorithm is well suited for solving even data clustering problems.