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Francisco Javier Ovalle-Martínez Julio Solano González Ivan Stojmenović 《Mobile Networks and Applications》2004,9(4):257-264

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** Hiroki Hayashi Mineichi Kudo Jun Toyama Masaru Shimbo 《Pattern Analysis & Applications》2001,4(1):20-27

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

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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

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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

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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**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

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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

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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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