改进的蚂蚁遗传混合算法及应用

车间作业调度问题是一个典型的NP-hard问题。分析了蚂蚁算法和遗传算法的特点,就遗传算子、交叉概率和变异概率上对传统遗传算法进行了改进;同时为了加速蚂蚁的搜索效率、减少迭代次数,重构了传统蚂蚁算法的下一个结点选择策略、信息素的局部更新策略,并将改进后的两个算法进行混合求车间作业调度的最优解。试验表明,算法的改进和混合提高了搜索效率及搜索结果的准确性。     

蚁群动态调度算法在混流车间作业调度的应用

混流车间作业调度是实际生产环节中的一个重要问题,也是制造系统生产管理的核心,同时实际的生产系统是一个动态生产环境.文中提出蚁群动态调度算法,通过实例具体分析并且跟传统的启发式算法相比较,实验结果证明蚁群动态调度算法对混流车间作业调度问题有较优的加工路径.     

多资源约束作业车间调度问题的模型研究

在柔性作业车间调度问题模型的基础上,针对资源约束作业车间调度问题及多目标柔性作业车间调度结合的问题,建立了资源约束下多目标柔性作业车间调度问题的数学模型,提出了启发式活动调度算法与遗传算法结合来对模型进行求解。选择操作采用轮盘赌选择方式;交叉操作采用基于活动启发式算法的交叉算子;变异操作采用基于领域搜索的变异。最后分析了一个具体调度结果实例。     

输入队列交换机的一种随机调度算法

对输入队列随机调度算法的取样问题进行了分析,指出由于输入队列的记忆特性,当前时隙的调度决策若具有最大权值,那么选取与这个最大权值相近的匹配作为下个时隙调度决策时的样点将以较大概率找到最大权值匹配.基于此本文设计了一种新的随机调度算法GALSA,GALSA利用演化策略来跟踪与每个时隙决策具有相近权值的匹配点.GALSA算法所需样点是O(N),因此其复杂性大大低于现有随机算法APSARA.且仿真结果表明GALSA的延迟性能与APSARA媲美.     

蚁群算法在作业调度中应用研究

合理配置车间作业调度中的各种资源可提高生产设备利用率与生产效率,降低生产成本。本文提出了一种求解混流装配线作业调度的蚁群算法,从信息素更新、状态转移概率论证该算法。通过计算目标函数与目标追随法、遗传算法、模拟退火算法比较,结果证明该算法对作业调度能够起到优化作用。     

求解作业车间调度问题的变邻域动态烟花算法

针对最小化最大完工时间的作业车间调度问题,提出一种基于变邻域搜索的动态烟花算法。变邻域搜索是一类重要的元启发式算法,在每一次迭代计算过程中通过搜索当前解的邻域得到一个改进的解,通过变邻域搜索方法可有效提高局部最优解的精度。与此同时,算法引入进化速度的概念,并通过进化速度计算烟花算法迭代过程中需更新的维度,实时调整相关参数,加快算法收敛,避免陷入局部最优。算例试验表明,该算法具有较好的鲁棒性,且能有效地提高算法搜索精度,加快收敛速度。     

一种移动云计算计算卸载算法及其仿真研究

移动云计算可以通过将计算任务从资源受限的移动设备卸载至云端以增强移动设备的计算能力,而如何实现约束条件下的能效计算卸载机制是当前的主要挑战。针对以降低移动设备能耗和应用完成时间为目标,将移动云计算的卸载问题形式化为博弈问题,提出一种分布式计算卸载博弈算法。分析了博弈的结构属性,证明了博弈算法在同质和异质无线访问情形下均能产生Nash均衡解,量化了算法在集中式最优解基础上的效率。数值仿真结果表明,在以能耗与完成时间衡量的总体能效代价性能上,新算法是有效可行的,在计算规模增大时依然拥有良好性能。     

协同进化多目标优化算法在车间调度中的应用

针对车间调度问题计算复杂度较高的特点,将协同进化多目标优化算法应用于车间调度问题。计算实例结果表明,协同进化多目标优化算法应用于车间调度问题不仅可以优化效果,而且能够在一定程度上提高计算效率。     

基于工作中心编码的车间作业调度算法研究

对于n个工件m台设备的作业调度问题,分析了基于作业工序编码算法存在的不足,提出了基于工作中心编码的遗传算法,即在不同染色体相同工作中心之间进行作业交换,有效扩大搜索解的空间。实验表明,基于工作中心编码算法的调度效果和调度效率均优于基于工序编码的算法。     

最小化最大延误无等待流水车间调度问题的禁忌搜索算法(英文)

为求解最小化最大延误无等待流水车间调度问题,提出了3个基于任务块交换的邻域,其中块交换邻域的规模为O(n4),块对换和简化块交换邻域的规模为O(n3).所提邻域的规模均大于现有邻域,因此可提高局部搜索算法的解质量.给出了3个邻域的加速性质,使一个相邻解的评估时间为常量,邻域的评估时间与其规模成正比.同基于支配规则的加速方法相比,所提出的加速性质适用于任何机器数.在禁忌搜索中比较了3个邻域,以及块对换和简化块交换邻域的并集.标准实例集上的计算结果表明:3个基于O(n3)邻域的禁忌搜索算法均好于现有算法;在所有的测试算法中,采用邻域并集的禁忌搜索算法的性能最好.     

Using Greedy algorithm: DBSCAN revisited II

INTRODUCTION Clustering groups dataset data into meaning-ful subclasses in such a way that minimizes theintra-differences and maximizes the in-ter-differences of these subclasses; and is one ofthe most widely studied problems in data mining.There are many application areas for clusteringtechniques, such as statistical data analysis, patternrecognition, image processing, and other businessprocesses etc. Many clustering algorithms havebeen proposed, in part…     

银行家安全算法的分析及改进

银行家算法通过执行安全算法来避免死锁。文章分析了传统银行家算法中安全算法的时间性能，提出在判断安全前，将进程按资源的需求量从小到大进行排序，可以改进传统安全算法的时间性能，将时间性能的量级从O(n^2)下降为O(n)。     

Using Greedy algorithm:DBSCAN revisited Ⅱ

The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clustering of Applications with Noise(DBSCAN)(Ester et al.,1996),and has the following advantages: first,Greedy algorithm substitutes for R*-tree(Bechmann et al.,1990)in DBSCAN to index the clustering space so that the clustering time cost is decreased to great extent and I/O memory load is reduced as well; second,the merging condition to approach to arbitrary-shaped clusters is designed carefully so that a single threshold can distinguish correctly all clusters in a large spatial dataset though some density-skewed clusters live in it. Finally,authors investigate a robotic navigation and test two artificial datasets by the proposed algorithm to verify its effectiveness and efficiency.     

Using Greedy algorithm: DBSCAN revisited II

The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clustering of Applications with Noise (DBSCAN) (Esteret al., 1996), and has the following advantages: first, Greedy algorithm substitutes forR ^*-tree (Bechmannet al., 1990) in DBSCAN to index the clustering space so that the clustering time cost is decreased to great extent and I/O memory load is reduced as well; second, the merging condition to approach to arbitrary-shaped clusters is designed carefully so that a single threshold can distinguish correctly all clusters in a large spatial dataset though some density-skewed clusters live in it. Finally, authors investigate a robotic navigation and test two artificial datasets by the proposed algorithm to verify its effectiveness and efficiency. Project (No. 2002AA2010) supported by the Hi-Tech Research and Development Program (863) of China     

GPU-based leaves contour generation algorithm

The implementation and optimization of the traditional contour generation algorithms are always proposed for the common processor. When processing high resolution images, the performance often exists low efficiency. A new graphics processing unit (GPU)-based algorithm is proposed to get the clear and integrated contour of leaves. Firstly we implement the classic Sobel operator of edge detection in GPU. Then a simple and effective method is designed to remove the fake edge and a heuristic algorithm is used to repair the broken edge. It is proved by the experiments that the results of our algorithm are natural and realistic in terms of morphology and can be good materials for the virtual plant.     

基于DNA计算的遗传算法解决最小生成树问题

给出了最小生成树问题（MST）的一个基于混合DNA计算的遗传算法模型。在该模型中,为了对最小生成树的解进行编码和解码,通过引入DNA计算,提出了一种最小生成树问题的改进遗传算法编码方案,该方案吸收了DNA计算和遗传算法的优点,具有固定的长度。为了搜索需要的最佳编码,引入遗传算法搜索技术,并给出了自适应的交叉算子和变异算子。最后,根据最小生成树问题的特点,通过实例仿真验证了所提出的基于DNA计算的遗传算法的有效性     

Optimal parallel algorithm for shortest-paths problem on interval graphs

This paper presents an efficient parallel algorithm for the shortest-path problem in interval graph for computing shortest-paths in a weighted interval graph that runs in O(n) time with n intervals in a graph. A linear processor CRCW algorithm for determining the shortest-paths in an interval graphs is given.     

Optimal parallel algorithm for shortest-paths problem on interval graphs

This paper presents an efficient parallel algorithm for the shortest-path problem in interval graph for computing shortest-paths in a weighted interval graph that runs in O(n) time with n intervals in a graph. A linear processor CRCW algorithm for determining the shortest-paths in an interval graphs is given.     

线性约束凸规划的一个新原-对偶路径-跟踪内点算法

In this paper, a primal-dual path-following interior-point algorithm for linearly constrained convex optimization （LCCO） is presented. The algorithm is based on a new technique for finding a class of search directions and the strategy of the central path. At each iteration, only full-Newton steps are used. Finally, the favorable polynomial complexity bound for the algorithm with the small-update method is deserved, namely, O（√nlog n/ε）.