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检索条件"任意字段=International Workshop on Multilevel Optimization - Algorithms and Applications"

共 1021 条记录，以下是161-170 订阅

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Delay-Aware Multi-Source Multicast Resource optimization in NFV-Enabled Network

Delay-Aware Multi-Source Multicast Resource Optimization in ...

引用

IEEE international Conference on Communications (IEEE ICC) / workshop on NOMA for 5G and Beyond

作者： Muhammad, Ali Qu, Long Assi, Chadi Concordia Univ Montreal PQ Canada Ningbo Univ Ningbo 315211 Peoples R China

ISBN: (纸本)9781728150895

Network Function Virtualization (NFV) is a transformation of traditional proprietary network designs to a more agile and software based environment. NFV architecture is considered as a key enabler for 5G as it offers the flexible deployment, reduced setup costs and less-time-to-market for the new services. Current studies on NFV in unicast transmission case can not be extended to multicast. Owing to the recent popularity and growing interest for live video streaming applications, efficient multicast solutions in NFV-enabled networks are needed. In this paper, we propose an NFV multicast resource optimization model as a Mixed Integer Linear Program (MILP) exploiting the use of multiple sources and considering the end-to-end delay and bandwidth requirements along with two heuristics algorithms. We evaluate the performance of the proposed algorithms on different network topologies. Simulation results prove that the proposed algorithms outperform the existing solution in terms of reduced bandwidth consumption and the delay values.

关键词： NFV delay multicast multi-source resource optimization

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Pixel-level image fusion using particle swarm optimization with local search

Pixel-level image fusion using particle swarm optimization w...

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international workshop on Intelligent Systems and applications

作者： Yang, Li-Ying School of Computer Science and Technology Xidian University Xi'an 710071 China

ISBN: (纸本)9781424498574

Pixel-level image fusion is widely used in many fields. We proposed a pixel-level image fusion algorithm based on particle swarm optimization with local search, that is, PSO-LS, which improves performance further. PSO-LS integrated the self-improvement mechanisms from memetic algorithms and can avoid local minimum in PSO. Experiments were carried out on two real world images. It is shown that fusion algorithm based on PSO-LS outperforms that based on PSO, and the former obtained optimal solution rapidly using fewer particles. © 2011 IEEE.

关键词： Particle swarm optimization (PSO)

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The cover number of a matrix and its algorithmic applications 17

The cover number of a matrix and its algorithmic application...

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17th international workshop on Approximation algorithms for Combinatorial optimization Problems, APPROX 2014 and the 18th international workshop on Randomization and Computation, RANDOM 2014

作者： Alon, Noga Lee, Troy Shraibman, Adi Sackler School of Mathematics and Blavatnik School of Computer Science Tel Aviv University Tel Aviv69978 Israel Institute for Advanced Study Princeton NJ08540 United States School of Physics and Mathematical Sciences Nanyang Technological University and Centre for Quantum Technologies Singapore School of Computer Science Academic College of Tel-Aviv Yaffo Israel

ISBN: (纸本)9783939897743

Given a matrix A, we study how many Ε-cubes are required to cover the convex hull of the columns of A. We show bounds on this cover number in terms of VC dimension and the γ2 norm and give algorithms for enumerating elements of a cover. This leads to algorithms for computing approximate Nash equilibria that unify and extend several previous results in the literature. Moreover, our approximation algorithms can be applied quite generally to a family of quadratic optimization problems that also includes finding the densest κ-by-k combinatorial rectangle of a matrix. In particular, for this problem we give the first quasi-polynomial time additive approximation algorithm that works for any matrix A ∈ [0, 1]m×n.. © Noga Alon, Troy Lee, and Adi Shraibman.

关键词： Approximation algorithms

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Comparison of evolutionary algorithms for design of sewer systems

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international workshop on Environmental Hydraulics

作者： Mora-Melia, Daniel Iglesias-Rey, Pedro L. Fuertes-Miquel, Vicente S. Martinez-Solano, Francisco J. Univ Politecn Valencia Ctr Multidisciplinar Modelac Fuidos E-46071 Valencia Spain

ISBN: (纸本)9780415566971

The evolutionary algorithms are methods of search of solutions that are based in the natural beginning of the evolution. Inside the evolutionary algorithms, we can find both the AG (Genetic algorithms) and PSO (Particle Swarm optimization), being one of many applications that present these algorithms the design of sewer systems. The work presents the application of these methods to the design of sewer systems. The design of these networks with evolutionary algorithms is of great interest if there is had present that allows us to choose a solution between the different alternatives that verify the conditions imposed for the pipes and the slope of the area. Finally, we realize an analysis of the obtained results, comparing both methods and extracting the possible advantages and disadvantages of applying each of these methods in the design of water sewer systems.

关键词： Genetic algorithms Particle Swarm optimization Sewer systems Water systems design

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Locality and Network-Aware Reduce Task Scheduling for Data-Intensive applications 5

Locality and Network-Aware Reduce Task Scheduling for Data-I...

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DataCloud 5th international workshop on Data Intensive Computing in the Clouds

作者： Arslan, Engin Shekhar, Mrigank Kosar, Tevfik SUNY Buffalo Buffalo NY 14260 USA Intel Corp Santa Clara CA USA

ISBN: (纸本)9781479970346

MapReduce is one of the leading programming frameworks to implement data-intensive applications by splitting the map and reduce tasks to distributed servers. Although there has been substantial amount of work on map task scheduling and optimization in the literature, the work on reduce task scheduling is very limited. Effective scheduling of the reduce tasks to the resources becomes especially important for the performance of data-intensive applications where large amounts of data are moved between the map and reduce tasks. In this paper, we propose a new algorithm (LoNARS) for reduce task scheduling, which takes both data locality and network traffic into consideration. Data locality awareness aims to schedule the reduce tasks closer to the map tasks to decrease the delay in data access as well as the amount of traffic pushed to the network. Network traffic awareness intends to distribute the traffic over the whole network and minimize the hotspots to reduce the effect of network congestion in data transfers. We have integrated LoNARS into Hadoop-1.2.1. Using our LoNARS algorithm, we achieved up to 15% gain in data shuffling time and up to 3-4% improvement in total job completion time compared to the other reduce task scheduling algorithms. Moreover, we reduced the amount of traffic on network switches by 15% which helps to save energy consumption considerably.

关键词： data handling distributed processing optimisation Data locality awareness data access data intensive applications distributed servers locality aware reduce task scheduling map task scheduling network aware reduce task scheduling optimization Benchmark testing Cost function Heart beat Scheduling Scheduling algorithms Servers TASK SCHEDULING Access to Information information retrieval Benchmark testing Heart beat Servers Scheduling algorithms Distributed processing data processing Cost functions Religious Missions distributed server Network Congestion Reduce Reduce

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An empirical study of simulated bee colony algorithm parameter values

An empirical study of simulated bee colony algorithm paramet...

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23rd international Conference on Computer applications in Industry and Engineering 2010, CAINE 2010 - Including SNA 2010 workshop

作者： McCaffrey, James D. Microsoft MSDN / Volt VTE Redmond WA 98052 United States

ISBN: (纸本)9781617820267

Simulated Bee Colony algorithms model the behavior of honey bees and can be used to find approximate solutions to difficult combinatorial optimization problems. One of the disadvantages of algorithms based on the behavior of natural systems is that these algorithms typically require the specification of several parameter values which are often highly sensitive with regards to their effect on solution convergence and accuracy. This study presents the results of the first known systematic investigation of Simulated Bee Colony parameter values. Experimental results targeting benchmark traveling salesman problem data sets suggest that 1.) the optimal percentages of active bees, inactive bees, and scout bees are approximately 75%, 10%, and 15% respectively;2.) the optimal probability that an inactive bee will be persuaded to accept the food source location of an active worker or scout bee is between 0.90 and 0.95;3.) the optimal probability that an active worker bee or a scout bee will accept a neighbor food source location with a worse measure of solution accuracy is between 0.001 and 0.005;4.) the optimal maximum number of times an active worker bee should visit a particular food source location solution is proportional to the number, Se, of possible neighbor solutions to any given solution and is between Se and 10 * S e.

关键词： Combinatorial optimization

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The optimization estimation of the yield per area based on intelligent computing

The optimization estimation of the yield per area based on i...

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2009 international workshop on Intelligent Systems and applications, ISA 2009

作者： Deng, Chang-Jian Department of Control engineering Chengdu University of Information Technology Chengdu Sichuan 610225 China

ISBN: (纸本)9781424438945

Estimation of the yield per area is useful to increase farmer's *** the paper, models of the yield per area based on special 0-1 knapsack problem and multidimensional knapsack problem are *** order to slove the problem based on these models, genetic algorithm and ant colony algorithm are designed in the paper. And a special coding graph is designed to slove the knapsack *** results of simulation are presented in the *** proves that the model of special 0-1 knapsack problem can slove the problem of optimization estimation of the yield per area based on intelligent computing. © 2009 IEEE.

关键词： Genetic algorithms

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Finite-State Methods and Natural Language Processing - Post-proceedings of the 7th international workshop FSMNLP 2008 - Volume 191 Frontiers in Artificial Intelligence and applications

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丛书名： Frontiers in Artificial Intelligence and applications 191

2009年

作者： Jakub Piskorski Bruce William Watson Anssi Yli-Jyr?

ISBN: (纸本)9781586039752

These proceedings contain the final versions of the papers presented at the 7th international workshop on Finite-State Methods and Natural Language Processing (FSMNLP), held in Ispra, Italy, on September 1112, 2008. The aim of the FSMNLP workshops is to bring together members of the research and industrial community working on finite-state based models in language technology, computational linguistics, web mining, linguistics and cognitive science on one hand, and on related theory and methods in fields such as computer science and mathematics on the other. Thus, the workshop series is a forum for researchers and practitioners working on applications as well as theoretical and implementation aspects. The special theme of FSMNLP 2008 was high performance finite-state devices in large-scale natural language text processing systems and applications. The papers in this publication cover a range of interesting NLP applications, including machine learning and translation, logic, computational phonology, morphology and semantics, data mining, information extraction and disambiguation, as well as programming, optimization and compression of finite-state networks. The applied methods include weighted algorithms, kernels and tree automata. In addition, relevant aspects of software engineering, standardization and European funding programs are *** Press is an international science, technical and medical publisher of high-quality books for academics, scientists, and professionals in all fields. Some of the areas we publish in: -Biomedicine -Oncology -Artificial intelligence -Databases and information systems -Maritime engineering -Nanotechnology -Geoengineering -All aspects of physics -E-governance -E-commerce -The knowledge economy -Urban studies -Arms control -Understanding and responding to terrorism -Medical informatics -Computer Sciences

关键词： Natural Language Processing Speech Recognition/Synthesis

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Proceedings of the 3rd IEEE international workshop on Cellular Neural Networks and their applications (CNNA-94)

Proceedings of the 3rd IEEE International Workshop on Cellul...

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Proceedings of the 3rd IEEE international workshop on Cellular Neural Networks and their applications (CNNA-94)

The proceedings contain 86 papers on the workshop conducted by the Institute of Electrical and Electronic Engineers (IEEE) on cellular neural networks and their applications. Topics discussed include algorithms, image processing, signal filtering and prediction, integrated circuit layout, VLSI circuits, discrete time cellular neural networks (DTCNN), control system analysis, robustness, computational complexity, mathematical models, convergence of numerical methods, state space methods, system stability, and optimization.

关键词： Cellular neural networks

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Artificial Intelligence applications and Innovations 1st ed. 2016

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丛书名： IFIP Advances in Information and Communication Technology

2016年

作者： Lazaros Iliadis Ilias Maglogiannis

ISBN: (数字)9783319449449

ISBN: (纸本)9783319449432

This book constitutes the refereed proceedings of the 12th IFIP WG 12.5 international Conference on Artificial Intelligence applications and Innovations, AIAI 2016, and three parallel workshops, held in Thessaloniki, Greece, in September 2016. The workshops are the Third workshop on New Methods and Tools for Big Data, MT4BD 2016, the 5th Mining Humanistic Data workshop, MHDW 2016, and the First workshop on 5G - Putting Intelligence to the Network Edge, 5G-PINE 2016. The 30 revised full papers and 8 short papers presented at the main conference were carefully reviewed and selected from 65 submissions. The 17 revised full papers and 7 short papers presented at the 3 parallel workshops were selected from 33 submissions. The papers cover a broad range of topics such as artificial neural networks, classification, clustering, control systems - robotics, data mining, engineering application of AI, environmental applications of AI, feature reduction, filtering, financial-economics modeling, fuzzy logic, genetic algorithms, hybrid systems, image and video processing, medical AI applications, multi-agent systems, ontology, optimization, pattern recognition, support vector machines, text mining, and Web-social media data AI modeling.

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