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1.
The up-link bandwidth in satellite networks and in advanced traffic wireless information system is very limited. A server broadcasts data files provided by different independent providers and accessed by many clients in a round-robin manner. The clients who access these files may have different patterns of access. Some clients may wish to access several files in any order (AND), some wish to access one out of several files (OR), and some clients may access a second file only after accessing another file (IMPLY). The goal of the server is to order the files in a way that minimizes the access time of the clients given some a priori knowledge of their access patterns. An appropriate clients–servers model was recently proposed by Bay-Noy, Naor and Schieber. They formulated three separate problems and proposed an algorithm that evaluates certain number of random permutations and chooses the one whose access time is minimized. In this paper, we formulate a combined AOI (AND-OR-IMPLY) problem, and propose to apply a parallel hill climbing algorithm (to each of the four problems), which begins from certain number of random permutations, and then applies hill climbing technique on each of them until there is no more improvement. The evaluation time of neighboring permutations generated in hill climbing process is optimized, so that it requires O(n) time per permutation instead of O(n 2) time required for evaluating access time of a random permutation, where n is the number of files the server broadcasts. Experiments indicate that the parallel hill climbing algorithm is O(n) times faster that random permutations method, both in terms of time needed to evaluate the same number of permutations, and time needed to provide a high quality solution. Thus the improvement is significant for broadcasting large number of files.  相似文献   
2.
Fast Labelling of Natural Scenes Using Enhanced Knowledge   总被引:1,自引:0,他引:1  
A new technique for labelling natural scenes is proposed. This technique labels disjoint regions on an image of a natural scene on the basis of knowledge about the relationship among objects. The proposed technique consists of three stages: (1) segmentation, (2) initial labelling, and (3) label improvement. One of the most promising previous techniques uses simulated annealing to find the solution, while our technique uses local hill-climbing with enhanced knowledge for speeding up the processing. Local hill-climbing is known to be easy to be captured by a local minimum. We solved this problem by enhancing the knowledge being used as constraints for the search. Our knowledge represents 1-to-n relationships among regions, pair-wise relationships of regions, and relative locations of the regions to the image. In addition, we introduced two region features: an entropy in intensity; and a linearity of contours of each region. The linearity evaluation aims to distinguish artificial objects from natural objects. The validity of the technique is supported by some experiments. These experiments showed that the proposed technique is much faster with the almost same accurate.  相似文献   
3.
Thispaper introduces ordinal hill climbing algorithms for addressingdiscrete manufacturing process design optimization problems usingcomputer simulation models. Ordinal hill climbing algorithmscombine the search space reduction feature of ordinal optimizationwith the global search feature of generalized hill climbing algorithms.By iteratively applying the ordinal optimization strategy withinthe generalized hill climbing algorithm framework, the resultinghybrid algorithm can be applied to intractable discrete optimizationproblems. Computational results on an integrated blade rotormanufacturing process design problem are presented to illustratethe application of the ordinal hill climbing algorithm. The relationshipbetween ordinal hill climbing algorithms and genetic algorithmsis also discussed. This discussion provides a framework for howthe ordinal hill climbing algorithm fits into currently appliedalgorithms, as well as to introduce a bridge between the twoalgorithms.  相似文献   
4.
模式搜索法在光纤有源自动对准中的应用   总被引:1,自引:1,他引:0  
提出基于模式搜索法的光纤有源自动对准算法,实现了多自由度同时寻优,解决了不同自由度之间的交叉耦合问题,提高了对准速度和对准精度。通过仿真与实验研究,与传统的爬山法进行对比。仿真结果表明,激光二极管与单模光纤五自由度对准时,模式搜索法只需20次迭代就可以找到最大点,收敛速度是爬山法的9倍。实验结果证明,横向调整两个自由度对准时,模式搜索法搜索速度比爬山法平均快10 s,定位成功率达到90%。  相似文献   
5.
王庆江  徐建良 《电子学报》2006,34(8):1420-1423
 为优化无中心式调度框架下网格作业的节点选择,提出了随机多起点爬山算法.为使多个起点均匀分布于网格,按随机选择邻居的重复次数的指数增长找出各起点.为反映合理的用户调度需求,用平均的并行计算能力加权的有界减慢率衡量节点选择.灵活调整网格工作负荷,对随机多起点爬山算法进行了全面评估.在网格负载不是很轻情况下,该算法能有效地在网格全局优化节点选择.  相似文献   
6.
基于近似密度函数的医学图像聚类分析研究   总被引:7,自引:0,他引:7  
针对医学图像数据难以用数学模型来表述和聚类的问题,提出一种基于近似密度函数的医学图像聚类分析方法.该方法采用核密度估计模型来构造近似密度函数,利用爬山策略来提取聚类模式.基于真实的人体腹部医学图像数据集的实验结果表明,该方法可以取得较好的聚类效果.  相似文献   
7.
求解SAT问题的改进粒子群优化算法   总被引:6,自引:5,他引:1  
贺毅朝  刘坤起 《计算机工程与设计》2006,27(15):2731-2733,2758
利用限制哆公式的相关理论将可满足性问题(SAT)等价转换为定义在{0,1}^n上的多项式函数优化问题,并将二进制粒子群优化算法(BPSO)与局部爬山搜索策略相结合,给出了一种求解SAT问题的新算法:基于局部爬山搜索的改进二进制粒子群优化算法(简称IBPSO).数值实验表明,对于随机产生的3-SAT问题测试实例,该算法的计算结果均优于著名的WalkSAT算法和SATI.3算法.  相似文献   
8.
F. Ho  M. Kamel 《Machine Learning》1998,33(2-3):155-177
A central issue in the design of cooperative multiagent systems is how to coordinate the behavior of the agents to meet the goals of the designer. Traditionally, this had been accomplished by hand-coding the coordination strategies. However, this task is complex due to the interactions that can take place among agents. Recent work in the area has focused on how strategies can be learned. Yet, many of these systems suffer from convergence, complexity and performance problems. This paper presents a new approach for learning multiagent coordination strategies that addresses these issues. The effectiveness of the technique is demonstrated using a synthetic domain and the predator and prey pursuit problem.  相似文献   
9.
We describe a general framework for learning perception-based navigational behaviors in autonomous mobile robots. A hierarchical behavior-based decomposition of the control architecture is used to facilitate efficient modular learning. Lower level reactive behaviors such as collision detection and obstacle avoidance are learned using a stochastic hill-climbing method while higher level goal-directed navigation is achieved using a self-organizing sparse distributed memory. The memory is initially trained by teleoperating the robot on a small number of paths within a given domain of interest. During training, the vectors in the sensory space as well as the motor space are continually adapted using a form of competitive learning to yield basis vectors that efficiently span the sensorimotor space. After training, the robot navigates from arbitrary locations to a desired goal location using motor output vectors computed by a saliency-based weighted averaging scheme. The pervasive problem of perceptual aliasing in finite-order Markovian environments is handled by allowing both current as well as the set of immediately preceding perceptual inputs to predict the motor output vector for the current time instant. We describe experimental and simulation results obtained using a mobile robot equipped with bump sensors, photosensors and infrared receivers, navigating within an enclosed obstacle-ridden arena. The results indicate that the method performs successfully in a number of navigational tasks exhibiting varying degrees of perceptual aliasing.  相似文献   
10.
We describe a general framework for learning perception-based navigational behaviors in autonomous mobile robots. A hierarchical behavior-based decomposition of the control architecture is used to facilitate efficient modular learning. Lower level reactive behaviors such as collision detection and obstacle avoidance are learned using a stochastic hill-climbing method while higher level goal-directed navigation is achieved using a self-organizing sparse distributed memory. The memory is initially trained by teleoperating the robot on a small number of paths within a given domain of interest. During training, the vectors in the sensory space as well as the motor space are continually adapted using a form of competitive learning to yield basis vectors that efficiently span the sensorimotor space. After training, the robot navigates from arbitrary locations to a desired goal location using motor output vectors computed by a saliency-based weighted averaging scheme. The pervasive problem of perceptual aliasing in finite-order Markovian environments is handled by allowing both current as well as the set of immediately preceding perceptual inputs to predict the motor output vector for the current time instant. We describe experimental and simulation results obtained using a mobile robot equipped with bump sensors, photosensors and infrared receivers, navigating within an enclosed obstacle-ridden arena. The results indicate that the method performs successfully in a number of navigational tasks exhibiting varying degrees of perceptual aliasing.  相似文献   
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