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2nd international workshop on Model-Driven Engineering on and for the Cloud, CloudMDE 2014, Co-located with the 17th international Conference on Model Driven Engineering Languages and Systems, MoDELS 2014

作者： Fleck, Martin Troya, Javier Langer, Philip Wimmer, Manuel Vienna University of Technology Business Informatics Group Austria

With the promise of seemingly unlimited resources and the flexible pay-as-you-go business model, more and more applications are moving to the cloud. However, to fully utilize the features offered by cloud providers, the existing applications need to be adapted accordingly. To support the developer in this task, different cloud computing patterns have been proposed. Nevertheless, selecting the most appropriate patterns and their configuration is still a major challenge. This is further complicated by the costs usually associated with deploying and testing an application in the cloud. In this paper, we encode the pattern selection problem as a model-based optimization problem to automatically compute good solutions of configured pattern applications. Particularly, we propose a two-phased approach, which is guided by user-defined constraints on the non-functional properties of the application. In the first phase, a preliminary set of promising solutions is computed using a genetic algorithm. In the second phase, this set of solutions is evaluated in more detail using model simulation. We demonstrate the proposed approach and show its feasibility by an initial case study.

关键词： Genetic algorithms

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3rd international workshop on Randomization and Approximation Techniques in Computer Science, and 2nd international workshop on Approximation algorithms for Combinatorial optimization Problems, RANDOM-APPROX 1999

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3rd international workshop on Randomization and Approximation Techniques in Computer Science and 2nd international workshop on Approximation algorithms for Combinatorial optimization Problems, RANDOM-APPROX 1999

ISBN: (纸本)3540663290

The proceedings contain 28 papers. The special focus in this conference is on Randomization, Approximation, and Combinatorial optimization. The topics include: Completeness and robustness properties of min-wise independent permutations;low discrepancy sets yield approximate min-wise independent permutation families;independent sets in hypergraphs with applications to routing via fixed paths;approximating minimum manhattan networks;approximation of multi-color discrepancy;a polynomial time approximation scheme for the multiple knapsack problem;set cover with requirements and costs evolving over time;multicoloring planar graphs and partial k-trees;testing the diameter of graphs;improved testing algorithms for monotonicity;linear consistency testing;improved bounds for sampling contingency tables;probabilistic and deterministic approximations of the permanent;improved derandomization of BPP using a hitting set generator;probabilistic construction of small strongly sum-free sets via large sidon sets;stochastic machine scheduling performance guarantees for LP-based priority policies;efficient redundant assignments under fault-tolerance constraints;scheduling with machine cost;a linear time approximation scheme for the job shop scheduling problem;randomized rounding for semidefinite programs;hardness results for the power range assignment problem in packet radio networks and a new approximation algorithm for the demand routing and slotting problem with unit demands on rings.

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Joint Power and Spectrum optimization in Wireless Localization Networks

Joint Power and Spectrum Optimization in Wireless Localizati...

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IEEE international Conference on Communications (ICC)

作者： Qin, Chuan Song, Liyuan Zhang, Tingting Shen, Yuan Molisch, Andreas F. Zhang, Qinyu Harbin Inst Technol Shenzhen Grad Sch Commun Engn Res Ctr Shenzhen Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China Univ Southern Calif Dept Elect Engn Los Angeles CA 90089 USA

ISBN: (纸本)9781467363051

Network localization is a key feature in many wireless services and applications. In typical rang-based localization techniques, "agents" try to perform position estimation through ranging with respect to "anchors" with known positions. Based on the definition of squared positional error bound (SPEB), the localization accuracy can be determined by the transmit power, carrier frequency and signal bandwidth, etc. This paper analyzes the joint power and spectrum allocation (JPSA) optimization problems in resource restricted wireless localization systems. We first formulate both interference-free and interference-limited JPSA problems. Since both problems are non-convex, we then develop two approximation algorithms based on single condensation and Taylor linearization. Both algorithms are able to find solutions close the global optimum. Numeric results validate our analysis, and show that, we are able to find optimal resource deployment in wireless localization networks based on the proposed frameworks.

关键词： Approximation algorithms

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Preface--Special issue on“Modern optimization and applications 2018”

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Journal of Computational Mathematics 2019年第6期37卷 I0001-I0002页

作者： Weimin Han Ge Wang Hongkai Zhao University of Iowa Rensselaer Polytechnic Institute University of California Irvine

This special issue contains eight selected papers from the international workshop on Modern optimization and applications,which was held over the three days,16-18 June 2018 at Academy of Mathematics and Systems Science,Chinese Academy of Sciences,*** conference brought together leading scientists,researchers,and practitioners from the world to exchange and shared ideas and approaches in using modern optimization techniques to model and solve real-world application problems from engineering,industry,and management.A prominent feature of this conference is the mixture of optimization theory,optimization methods,and practice of mathematical *** conference provided a forum for researchers from academia to present their latest theoretical results and for practitioners from industry to describe their real-world applications,and discuss with participants the best way to construct suitable optimization models and how to find algorithms capable of solving these models.

关键词： optimization. optimization industry

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Independent sets in hypergraphs with applications to routing via fixed paths 3rd

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作者： Alon, Noga Arad, Uri Azar, Yossi Department of Mathematics and Computer Science Tel-Aviv University Israel Dept. of Computer Science Tel-Aviv University Tel-Aviv69978 Israel Dept. of Computer Science Tel-Aviv University Tel-Aviv69978 Israel

ISBN: (纸本)3540663290

The problem of finding a large independent set in a hyper-graph by an online algorithm is considered. We provide bounds for the best possible performance ratio of deterministic vs. randomized and non-preemptive vs. preemptive algorithms. Applying these results we prove bounds for the performance of online algorithms for routing problems via fixed paths over networks. © Springer-Verlag Berlin Heidelberg 1999.

关键词： Artificial intelligence

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Internet and Network Economics: Third international workshop, WINE 2007 Proceedings

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3rd international workshop on Internet and Network Economics, WINE 2007

ISBN: (纸本)9783540771043

The proceedings contain 64 papers. The topics discussed include: my favorite simplical complex and some of its applications;markets and the primal-dual paradigm;the computation of equilibria;a note on equilibrium pricing as convex optimization;new algorithms for approximate Nash equilibria in bimatrix games;a unified approach to congestion games and two-sided markets;an optimization approach for approximate Nash equilibria;gradient-based algorithms for finding Nash equilibria in extensive form games;bluffing and strategic reticence in prediction markets;information sharing communities;competitive safety strategies in position auctions;maintaining equilibria during exploration in sponsored search auctions;stochastic models for budget optimization in search-based advertising;auctions with revenue guarantees for sponsored search;cost-balancing tolls for atomic network congestion games;and sponsored search with contexts.

关键词： Electronic commerce

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A Multi-Agent Intelligence Hybrid System Technique for Detection and Defense of DDoS Attacks

A Multi-Agent Intelligence Hybrid System Technique for Detec...

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workshop on Computer Architecture, Embedded Systems, SoC, and VLSI/EDA / international Computer Symposium (ICS)

作者： Chen, Hsia-Hsiang Huang, Shih-Kun Natl Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan Natl Chiao Tung Univ Informat Technol Serv Ctr Hsinchu Taiwan

ISBN: (纸本)9781614994848;9781614994831

In the paper, we propose the distributed detection and identification multi-agent system (DDIMAS) framework that is the first attempt to apply in solving distributed denial of service (DDoS) problem. It includes three stages which are information heuristic rule, meta-heuristic algorithm and backward and forward search (BFS) rule, respectively. Moreover, the framework is a flexible architecture that can incorporate into other algorithms or rules to improve the overall performance. From the evaluation design, the experiment results show that our method is with higher detection rate and better accuracy than standard repositories. The proposed framework resolves issues in other swarm optimization algorithms and reveals that the performance of DDIMAS is better than existing methods and the adaptive meta-heuristic algorithm framework outperforms other methods for detecting DDoS attacks.

关键词： distributed denial of service meta-heuristic algorithm swarm optimization algorithm

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An Improved Lightning Attachment Procedure optimization Algorithm for Function optimization 10

An Improved Lightning Attachment Procedure Optimization Algo...

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10th IEEE international Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and applications (IDAACS)

作者： Sun, Shuang Ye, Zhiwei Sun, Yiheng Zhan, Sikai Yu, Han Yao, Quanfeng Hubei Univ Technol Sch Comp Sci Wuhan 430068 Peoples R China Wuhan Fiberhome Tech Serv Co LTD Wuhan Peoples R China

ISBN: (纸本)9781728140698

The hybridization of different meta-heuristic algorithms is for expanding the synergies of a single optimization method used alone and achieving a better optimum search performance. In this work, we proposed a hybrid optimization method combining lightning attachment procedure optimization algorithm (LAPO) and the gravitational search algorithm (GSA), and applied to the function optimization. In order to integrate the excellent exploitation performance of LAPO with the great exploration capability of the GSA to synthesize the strength of each algorithm, we utilized series hybrid mode and some benchmark test functions were employed for evaluating and comparing the performance with the standard mode. Meanwhile, the most commonly used algorithm, particle swarm optimization algorithm is also used for contrast. The experiment results show that the hybrid algorithm obtains better time efficiency and convergence capacity, also have a great ability to avoid local optimums.

关键词： hybrid optimization lightning attachment procedure optimization algorithm gravitational search algorithm function optimization

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BAYESIAN NETWORK STRUCTURE optimization FOR IMPROVED DESIGN SPACE MAPPING FOR DESIGN EXPLORATION WITH MATERIALS DESIGN applications

BAYESIAN NETWORK STRUCTURE OPTIMIZATION FOR IMPROVED DESIGN ...

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ASME international Design Engineering Technical Conferences / Computers and Information in Engineering Conference (IDETC/CIE 2017)

作者： Sharpe, Conner Morris, Clinton Goldsberry, Benjamin Seepersad, Carolyn Conner Haberman, Michael R. Univ Texas Austin Mech Engn Dept Austin TX 78712 USA

ISBN: (纸本)9780791858134

Modern design problems present both opportunities and challenges, including multifunctionality, high dimensionality, highly nonlinear multimodal responses, and multiple levels or scales. These factors are particularly important in materials design problems and make it difficult for traditional optimization algorithms to search the space effectively, and designer intuition is often insufficient in problems of this complexity. Efficient machine learning algorithms can map complex design spaces to help designers quickly identify promising regions of the design space. In particular, Bayesian network classifiers (BNCs) have been demonstrated as effective tools for top-down design of complex multilevel problems. The most common instantiations of BNCs assume that all design variables are independent. This assumption reduces computational cost, but can limit accuracy especially in engineering problems with interacting factors. The ability to learn representative network structures from data could provide accurate maps of the design space with limited computational expense. Population-based stochastic optimization techniques such as genetic algorithms (GAs) are ideal for optimizing networks because they accommodate discrete, combinatorial, and multimodal problems. Our approach utilizes GAs to identify optimal networks based on limited training sets so that future test points can be classified as accurately and efficiently as possible. This method is first tested on a common machine learning data set, and then demonstrated on a sample design problem of a composite material subjected to a planar sound wave.

关键词： Genetic algorithms

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Particle swarm optimization and its applications to VLSI design and video technology

Particle swarm optimization and its applications to VLSI des...

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2005 IEEE international workshop on VLSI Design and Video Technology, IWVDVT 2005

作者： Eberhart, Russell Shi, Yuhui Purdue University United States

ISBN: (纸本)0780390067

Particle Swarm optimization (PSO) is one of the evolutionary computation techniques. It is originally inspired from the social behavior of flying birds. Each particle represents a potential solution to the problem to be solved. Particles fly through the solution space. Each particle's velocity is dynamically updated according to its own flying experience and its companion's flying experience with the expectation that the particles will fly towards better and better solution areas. In this talk, the basic concept of PSO and its most recent development will be discussed. The topic will be divided into five parts: algorithms, topology, parameters, comparison, and applications. The different versions of PSO algorithms: the real value PSO, which is the original version of PSO and is most suited for solving real-value problems;the binary version of PSO, which is designed to solve binary problem;and the discrete version of PSO, which is good for solving the event-based problems. The topology of the PSO. Originally, there are two different PSO topologies. They are global version PSO and local version PSO, where each particle has direct interaction with all other particles in the population or in its neighborhood, respectively. To date, different topologies have been designed for the PSO implementations and their performances have been compared to see the effect of topology on the PSO's performance. The parameters of PSO and their impacts on the PSO's performance, especially the inertia weight and constriction factor. The comparison between the PSO and the other evolutionary computation techniques, such as genetic algorithms, and evolutionary programming. Also discussed is the trend to merge PSO with other EC techniques. The PSO's applications to the VLSI design and video technology. The focus will be on particle (or problem) representations and the fitness function designs.

关键词： VLSI circuits

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