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991.
992.
Tongqing Qiu Edward Chan Mao Ye Guihai Chen Ben Y. Zhao 《The Journal of supercomputing》2009,48(1):15-42
A self-organizing peer-to-peer system is built upon an application level overlay, whose topology is independent of an underlying
physical network. A well-routed message path in such systems may result in a long delay and excessive traffic due to the mismatch
between logical and physical networks. In order to solve this problem, we present a family of Peer-exchange Routing Optimization
Protocols (PROP) to reconstruct the overlay. It includes two policies: PROP-G for generic condition and PROP-O for optimized
one. Both theoretical analysis and simulation experiments show that these two protocols greatly reduce the average latency
of the overlay and achieve a better logical topology with low overhead. Their overall performance can be further improved
if combined with other recent approaches. Specifically, PROP-G can be easily applied to both structured and unstructured systems
without the loss of their primary characteristics, such as efficient routing and anonymity. PROP-O, on the other hand, is
more efficient, especially in a heterogenous environment where nodes have different processing capabilities.
相似文献
Edward ChanEmail: |
993.
A particle is treated as a whole individual in all researches on particle swarm optimization (PSO) currently, these are not concerned with the information of every particle’s dimensional vector. A visual modeling method describing particle’s dimensional vector behavior is presented in this paper. Based on the analysis of visual modeling, the reason for premature convergence and diversity loss in PSO is explained, and a new modified algorithm is proposed to ensure the rational flight of every particle’s dimensional component. Meanwhile, two parameters of particle-distribution-degree and particle-dimension-distance are introduced into the proposed algorithm in order to avoid premature convergence. Simulation results of the new PSO algorithm show that it has a better ability of finding the global optimum, and still keeps a rapid convergence as with the standard PSO. 相似文献
994.
Learning algorithm for multimodal optimization 总被引:1,自引:0,他引:1
We present a new evolutionary algorithm—“learning algorithm” for multimodal optimization. The scheme for reproducing a new generation is very simple. Control parameters, of the length of the list of historical best solutions and the “learning probability” of the current solutions being moved towards the current best solutions and towards the historical ones, are used to assign different search intensities to different parts of the feasible area and to direct the updating of the current solutions. Results of numerical tests on minimization of the 2D Schaffer function, the 2D Shubert function and the 10D Ackley function show that this algorithm is effective and efficient in finding multiple global solutions of multimodal optimization problems. 相似文献
995.
Jun Wang Sijing Zhang Carsten Maple Zhengxu Zhao 《Computer Standards & Interfaces》2009,31(3):557-565
Synchronous bandwidth, defined as the maximum time a node is allowed to send its synchronous messages while holding the token, is a sensitive parameter for deadline guarantees of synchronous messages in any timed token network. In order to offer such guarantees, synchronous bandwidth has to be allocated carefully to each individual node. This paper studies the synchronous bandwidth allocated to those synchronous message streams whose deadlines are less than twice the Target Token Rotation Time (TTRT). A new approach for allocating synchronous bandwidth to such streams, which can be used with any previously published local synchronous bandwidth allocation (SBA) for guaranteeing a general synchronous message set with its minimum deadline (Dmin) no less than 2 · TTRT, is proposed. The proposed scheme can be applied efficiently in practice to any general synchronous message set with Dmin > TTRT. Numerical examples are presented to demonstrate the enhanced performance of this new local scheme over any of the previously published local SBA schemes. 相似文献
996.
This paper focuses on the performance evaluation of complex man-made systems, such as assembly lines, electric power grid,
traffic systems, and various paper processing bureaucracies, etc. For such problems, applying the traditional optimization
tool of mathematical programming and gradient descent procedures of continuous variables optimization are often inappropriate
or infeasible, as the design variables are usually discrete and the accurate evaluation of the system performance via a simulation
model can take too much calculation. General search type and heuristic methods are the only two methods to tackle the problems.
However, the “goodness” of heuristic methods is generally difficult to quantify while search methods often involve extensive
evaluation of systems at many design choices in a large search space using a simulation model resulting in an infeasible computation
burden. The purpose of this paper is to address these difficulties simultaneously by extending the recently developed methodology
of Ordinal Optimization (OO). Uniform samples are taken out from the whole search space and evaluated with a crude but computationally
easy model when applying OO. And, we argue, after ordering via the crude performance estimates, that the lined-up uniform
samples can be seen as an approximate ruler. By comparing the heuristic design with such a ruler, we can quantify the heuristic
design, just as we measure the length of an object with a ruler. In a previous paper we showed how to quantify a heuristic
design for a special case but we did not have the OO ruler idea at that time. In this paper we propose the OO ruler idea and
extend the quantifying method to the general case and the multiple independent results case. Experimental results of applying
the ruler are also given to illustrate the utility of this approach.
Zhen Shen received the B.E. degree from Department of Automation, Tsinghua University, Beijing, China in 2004. Currently, he is a Ph.D. candidate of Center for Intelligent and Networked Systems (CFINS), Department of Automation, Tsinghua University. He was a Visiting Scholar from Oct. 2007 to Apr. 2008 at Department of Manufacturing Engineering and Center for Information and Systems Engineering, Boston University, MA, USA. He specializes in the area of the discrete event dynamic systems (DEDS) theory and applications, and the optimization of complex systems. He is a student member of IEEE. Yu-Chi Ho received his S.B. and S.M. degrees in Electrical Engineering from M.I.T. and his Ph.D. in Applied Mathematics from Harvard University. Except for three years of full time industrial work he has been on the Harvard faculty. Since 1969 he has been Gordon McKay Professor of Engineering and Applied Mathematics. In 1988, he was appointed to the T. Jefferson Coolidge Chair in Applied Mathematics and Gordon McKay Professor of Systems Engineering at Harvard and as visiting professor to the Cockrell Family Regent’s Chair in Engineering at the University of Texas, Austin. In 2001, he retired from teaching duties at Harvard and became a Research Professor (2001–2006) and also was appointed to be a chair professor and chief scientist (part time), at the Center for Intelligent and Networked Systems (CFINS), Department of Automation, Tsinghua University, Beijing China. Qian-Chuan Zhao received the B.E. degree in automatic control in July 1992, the B.S. degree in applied mathematics in July 1992, and the Ph.D. degree in control theory and its applications in July 1996, all from Tsinghua University, Beijing, China. He is currently a Professor and Associate Director of the Center for Intelligent and Networked Systems (CFINS), Department of Automation, Tsinghua University. He was a Visiting Scholar at Carnegie Mellon University, Pittsburgh, PA, and Harvard University, Cambridge, MA, in 2000 and 2002, respectively. He was a Visiting Professor at Cornell University, Ithaca, NY, in 2006. His research interests include discrete event dynamic systems (DEDS) theory and applications, optimization of complex systems, and wireless sensor networks. Dr. Zhao is an associate editor for the Journal of Optimization Theory and Applications. 相似文献
Zhen ShenEmail: |
Zhen Shen received the B.E. degree from Department of Automation, Tsinghua University, Beijing, China in 2004. Currently, he is a Ph.D. candidate of Center for Intelligent and Networked Systems (CFINS), Department of Automation, Tsinghua University. He was a Visiting Scholar from Oct. 2007 to Apr. 2008 at Department of Manufacturing Engineering and Center for Information and Systems Engineering, Boston University, MA, USA. He specializes in the area of the discrete event dynamic systems (DEDS) theory and applications, and the optimization of complex systems. He is a student member of IEEE. Yu-Chi Ho received his S.B. and S.M. degrees in Electrical Engineering from M.I.T. and his Ph.D. in Applied Mathematics from Harvard University. Except for three years of full time industrial work he has been on the Harvard faculty. Since 1969 he has been Gordon McKay Professor of Engineering and Applied Mathematics. In 1988, he was appointed to the T. Jefferson Coolidge Chair in Applied Mathematics and Gordon McKay Professor of Systems Engineering at Harvard and as visiting professor to the Cockrell Family Regent’s Chair in Engineering at the University of Texas, Austin. In 2001, he retired from teaching duties at Harvard and became a Research Professor (2001–2006) and also was appointed to be a chair professor and chief scientist (part time), at the Center for Intelligent and Networked Systems (CFINS), Department of Automation, Tsinghua University, Beijing China. Qian-Chuan Zhao received the B.E. degree in automatic control in July 1992, the B.S. degree in applied mathematics in July 1992, and the Ph.D. degree in control theory and its applications in July 1996, all from Tsinghua University, Beijing, China. He is currently a Professor and Associate Director of the Center for Intelligent and Networked Systems (CFINS), Department of Automation, Tsinghua University. He was a Visiting Scholar at Carnegie Mellon University, Pittsburgh, PA, and Harvard University, Cambridge, MA, in 2000 and 2002, respectively. He was a Visiting Professor at Cornell University, Ithaca, NY, in 2006. His research interests include discrete event dynamic systems (DEDS) theory and applications, optimization of complex systems, and wireless sensor networks. Dr. Zhao is an associate editor for the Journal of Optimization Theory and Applications. 相似文献
997.
The conventional self-organizing feature map (SOM) algorithm is usually interpreted as a computational model, which can capture
main features of computational maps in the brain. In this paper, we present a variant of the SOM algorithm called the SOM-based
optimization (SOMO) algorithm. The development of the SOMO algorithm was motivated by exploring the possibility of applying
the SOM algorithm in continuous optimization problems. Through the self-organizing process, good solutions to an optimization
problem can be simultaneously explored and exploited by the SOMO algorithm. In our opinion, the SOMO algorithm not only can
be regarded as a biologically inspired computational model but also may be regarded as a new approach to a model of social
influence and social learning. Several simulations are used to illustrate the effectiveness of the proposed optimization algorithm. 相似文献
998.
Automatic image annotation (AIA) is an effective technology to improve the performance of image retrieval. In this paper, we propose a novel AIA scheme based on hidden Markov model (HMM). Compared with the previous HMM-based annotation methods, SVM based semi-supervised learning, i.e. transductive SVM (TSVM), is triggered out for remarkably boosting the reliability of HMM with less users’ labeling effort involved (denoted by TSVM-HMM). This guarantees that the proposed TSVM-HMM based annotation scheme integrates the discriminative classification with the generative model to mutually complete their advantages. In addition, not only the relevance model between the visual content of images and the textual keywords but also the property of keyword correlation is exploited in the proposed AIA scheme. Particularly, to establish an enhanced correlation network among keywords, both co-occurrence based and WordNet based correlation techniques are well fused and are able to be helpful for benefiting from each other. The final experimental results reveal that the better annotation performance can be achieved at less labeled training images. 相似文献
999.
Single multiplicative neuron model is a novel neural network model introduced recently, which has been used for time series prediction and function approximation. The model is based on a polynomial architecture that is the product of linear functions in different dimensions of the space. Particle swarm optimization (PSO), a global optimization method, is proposed to train the single neuron model in this paper. An improved version of the original PSO, cooperative random learning particle swarm optimization (CRPSO), is put forward to enhance the performance of the conventional PSO. The proposed CRPSO, PSO, back-propagation algorithm and genetic algorithm are employed to train the model for three well-known time series prediction problems. The experimental results demonstrate the superiority of CRPSO-based neuron model in efficiency and robustness over the other three algorithms. 相似文献
1000.
Liang Kong Yunzhuan Zhao Kaijin Hu Dehua Li Hongzhi Zhou Ziyan Wu Baolin Liu 《Advances in Engineering Software》2009,40(7):474-478
In this paper, effects of the implant thread pitch on the maximum Von Mises stresses were evaluated in jaw bones and implant–abutment complex by a finite element method. The thread pitch ranged from 0.5 mm to 1.6 mm. Results suggested that under axial load, the maximum equivalent stresses in cortical bone, cancellous bone and implant–abutment complex decreased by 6.7%, 55.2% and 22.3%, respectively with the variation of thread pitch; and under buccolingual load, 2.7%, 22.4% and 13.0%, respectively. When thread pitch exceeded 0.8 mm, minimum stresses were obtained. Data indicated that cancellous bone was more sensitive to thread pitch than cortical bone did; thread pitch played a great role in protecting dental implant under axial load than under buccolingual load; and thread pitch exceeding 0.8 mm were optimal selection for a screwed implant by biomechanical consideration. 相似文献