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检索条件"任意字段=2016 International Congress on Computation Algorithms in Engineering"

共 1728 条记录，以下是91-100 订阅

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Parallel computation of a mixed convection problem using fully-coupled and segregated algorithms

Parallel computation of a mixed convection problem using ful...

引用

2004 ASME international Mechanical engineering congress and Exposition, IMECE

作者： Darbandi, Masoud Banaeizadeh, Araz Schneider, Gerry E. Department of Aerospace Engineering Sharif University of Technology Tehran P.O. Box 11365-8639 Iran Department of Mechanical Engineering University of Waterloo Waterloo Ont. N2L 3G1 Canada

In this work, parallel solution of the Navier-Stokes equations for a mixed convection heat problem is achieved using a finite-element-based finite-volume method in fully coupled and semi coupled algorithms. A major drawback with the implicit methods is the need for solving the huge set of linear algebraic equations in large scale problems. The current parallel computation is developed on distributed memory machines. The matrix decomposition and solution are carried out using PETSc library. In the fully coupled algorithm, there is a 36-diagonal global matrix for the two-dimensional governing equations. In order to reduce the computational time, the matrix is suitably broken in several sub-matrices and they are subsequently solved in a segregated manner. This approach results in four 9-diagonal matrices. Different sparse solver algorithms are utilized to solve a mixed natural-forced convection problem using either fully-coupled or semi-coupled algorithms. The performance of the solvers are then investigated in solving on a distributed computing environment. The study shows that the iteration run time considerably decreases although the overall run time of the fully coupled algorithm still looks better. Copyright © 2004 by ASME.

关键词： Heat convection

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Interference-aware virtual machine placement in cloud computing systems

Interference-aware virtual machine placement in cloud comput...

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2012 international Conference on Computer and Information Science, ICCIS 2012 - A Conference of World engineering, Science and Technology congress, ESTCON 2012

作者： Lin, Jenn-Wei Chen, Chien-Hung Department of Computer Science and Information Engineering Fu Jen Catholic University Taiwan

ISBN: (纸本)9781467319386

Cloud computing provides scalable computing and storage resources. These scalable resources can be dynamically organized as many virtual machines (VMs) to run user applications based on a pay-per-use basis. This paper investigates the interference-aware VM placement (IAVMP) problem. In addition to fully exploiting the resources of PMs, the IAVMP problem considers the quality of service (QoS) requirements of user applications and the VM interference reduction. We first formulate the IAVMP problem by an Integer Linear Programming (ILP) model to solve it optimally. Due to the computation complexity of the ILP model, we also propose a polynomial-time heuristic algorithm to efficiently solve the IAVMP problem. Finally, simulation experiments are performed to demonstrate the effectiveness of the proposed heuristic algorithm by comparing with other VM placement algorithms. © 2012 IEEE.

关键词： Quality of service

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A Two-Stage Evolutionary Framework for Multi-Objective Optimization 13

A Two-Stage Evolutionary Framework for Multi-Objective Optim...

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13th IEEE congress on Evolutionary computation, CEC 2024

作者： Chen, Peng Liang, Jing Qiao, Kangjia Suganthan, Ponnuthurai Nagaratnam Ban, Xuanxuan School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China School of Electrical Engineering and Automation Henan Institute of Technology Xinxiang China Kindi Computing Research Center Qatar University Doha Qatar

ISBN: (纸本)9798350308365

In the field of evolutionary multi-objective optimization, the approximation of the Pareto front (PF) is achieved by utilizing a collection of representative candidate solutions that exhibit desirable convergence and diversity. Although several multi-objective evolutionary algorithms (MOEAs) have been designed, they still have difficulties in keeping balance between convergence and diversity of population. To better solve multi-objective optimization problems (MOPs), this paper proposes a Two-stage Evolutionary Framework For Multi-objective Opti-mization (TEMOF). Literally, algorithms are divided into two stages to enhance the search capability of the population. During the initial half of evolutions, parental selection is exclusively conducted from the primary population. Additionally, we not only perform environmental selection on the current population, but we also establish an external archive to store individuals situated on the first PF. Subsequently, in the second stage, parents are randomly chosen either from the population or the archive. In the experiments, one classic MOEA and two state-of-the-art MOEAs are integrated into the framework to form three new algorithms. The experimental results demonstrate the superior and robust performance of the proposed framework across a wide range of MOPs. Besides, the winner among three new algorithms is compared with several existing MOEAs and shows better results. Meanwhile, we conclude the reasons that why the two-stage framework is effect for the existing benchmark functions. © 2024 IEEE.

关键词： Optimization algorithms

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Comparison of differential grouping and random grouping methods on sCCPSO for large-scale constrained optimization

Comparison of differential grouping and random grouping meth...

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2016 IEEE congress on Evolutionary computation, CEC 2016

作者： Peng, Chen Hui, Qing Department of Electrical and Computer Engineering University of Nebraska-Lincoln LincolnNE68588-0511 United States

ISBN: (纸本)9781509006229

Differential grouping (DG) is a pregrouping method in some unconstrained cooperative coevolutionary algorithms where the grouping scheme is predetermined, while random grouping (RG) is a method where the grouping schemes are dynamically changed according to some criteria. As the first nature-inspired method for large-scale constrained optimization, the ϵ-constrained cooperative coevolutionary particle swarm optimization ϵCCPSO) algorithmic framework has been recently proposed and studied with different RG techniques, e.g., ϵCCPSOw2. This paper aims to compare the effectiveness of DG and RG methods applied on the ϵCCPSO framework, using ϵCCPSOw2 and two proposed DG implementations, i.e., ϵCCPSOdg and ϵCCPSOdg2, for large-scale constrained problems. The results show that, while ϵCCPSOw2 possesses the fastest convergence rate on the fitness values of most problems, ϵCCPSOdg2 has better constraint violations minimization ability when solving some problems with nonseparable equality constraints. In addition, an improvement of the DG method is proposed, increasing the speed to approximately twice the original. © 2016 IEEE.

关键词： Particle swarm optimization (PSO)

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Long-term orbit propagation using symplectic integration algorithms 67

Long-term orbit propagation using symplectic integration alg...

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67th international Astronautical congress, IAC 2016

作者： Kuppa, Koundinya Spencer, David B. Department of Aerospace Engineering Pennsylvania State University 229 Hammond Building University ParkPA United States

Understanding the evolution of satellite orbits in the long-term is of great importance in astrodynamics. In order to achieve this, accurate propagation of the orbital dynamics of the satellite is required. This paper attempts to implement and evaluate a class of numerical integration methods known as symplectic algorithms. This class of algorithms is highly regarded in scientific applications, especially in long-term studies. The paper will demonstrate the superior accuracy and efficient speed of several algorithms of this class and obtain long-term state of satellites under the several influencing forces. Other applications such as an orbit determination process and a long-term stability analysis of a satellite in orbit around a Lagrange point in the restricted three-body problem are also performed to test the versatility of the algorithms. Within each application, several cases with different values for parameters such as the time step and duration are executed. In addition, long-term orbital evolution of a satellite in various orbital regimes is conducted. Also, applications of these algorithms in other problems in astrodynamics are discussed. Copyright © 2016 by the international Astronautical Federation (IAF). All rights reserved.

关键词： Satellites

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A Data-Driven Optimization Method for Strongly Non-Separable Mixed-Integer Problems 13

A Data-Driven Optimization Method for Strongly Non-Separable...

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13th IEEE congress on Evolutionary computation, CEC 2024

作者： Sato, Takahiro Graduate School of Engineering Muroran Institute of Technology Muroran Japan

ISBN: (纸本)9798350308365

This paper presents a data-driven optimization method based on tree search-based reinforcement learning to solve strongly non-separable mixed-integer problems. With this method, some variables that mainly affect performance and non-separability are stochastically determined through a modified tree search. Then, other variables are stochastically estimated and optimized by evolutionary algorithms. These probabilities are adapted with a reinforcement learning approach. As a result, the present method makes it possible to easily solve non-separable mixed-integer problems. Moreover, in the reinforcement learning, previous optimization results can be utilized in the next optimizations. The present method is applied to a design problem of an induction motor, which is one of the practical engineering and non-separable mixed-integer optimization problems. It is shown that the present method can solve the design problem under various conditions. © 2024 IEEE.

关键词： Mixed-integer linear programming

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A Study on Prediction of Rainfall using datamining technique

A Study on Prediction of Rainfall using datamining technique

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2016 international Conference on Inventive computation Technologies, ICICT 2016

作者： Senthil Kumar, R. Ramesh, C. Department of Computer Science Engineering Satyabama University Chennai India

India is an agricultural country and its economy is largely based upon crop productivity and rainfall. For analyzing the crop productivity, rainfall prediction is require and necessary to all farmers. Rainfall Prediction is the application of science and technology to predict the state of the atmosphere. It is important to exactly determine the rainfall for effective use of water resources, crop productivity and pre planning of water structures. Using different data mining techniques it can predict rainfall. Data mining techniques are used to estimate the rainfall numerically. This paper focuses some of the popular data mining algorithms for rainfall prediction. Naive Bayes, K- Nearest Neighbour algorithm, Decision Tree, Neural Network and fuzzy logic are some of the algorithms compared in this paper. From that comparison, it can analyze which method gives better accuracy for rainfall prediction.

关键词： Fuzzy logic

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Insight to AMP and ADMM based Sparse signal reconstruction

Insight to AMP and ADMM based Sparse signal reconstruction

引用

2016 international Conference on Communication and Signal Processing, ICCSP 2016

作者： Subramanian, Surya Gandhiraj, R. Amrita School of Engineering Amrita Vishwa Vidhyapeetham University Coimbatore India

ISBN: (纸本)9781509003969

Compressive sensing (CS) is a rising field which have already overcome Nyquist rate. It enables sparse signal recovery with the help of efficient algorithms. It reduces memory requirements and cost of computation. Signal recovery is an important phase in reconstruction of sparse signal. In this paper a comparison is made between two reconstruction algorithms approximate message passing algorithm (AMP) and ADMM (Alternate direction method of multipliers) for solving the basis pursuit problem. © 2016 IEEE.

关键词： Compressed sensing

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A comparative analysis of iterative MapReduce systems 16

A comparative analysis of iterative MapReduce systems

引用

6th international Conference on Emerging Databases: Technologies, Applications, and Theory, EDB 2016

作者： Kang, Minseo Lee, Jae-Gil Graduate School of Knowledge Service Engineering KAIST Korea Republic of

ISBN: (纸本)9781450347549

Since the development of MapReduce, there have been several efforts to extend data mining and machine learning algorithms for MapReduce. Many of those algorithms are iterative by nature. In order to process them efficiently, Spark as well as research prototypes such as HaLoop, iMapReduce, and Twister are proposed with solutions to iterative computation. In this paper, we thoroughly examine the pros and cons of each system. © 2016 ACM.

关键词： MapReduce

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Hybrid Immune PSO Algorithm for engineering Optimization Problems 12

Hybrid Immune PSO Algorithm for Engineering Optimization Pro...

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12th international Conference on Natural computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Fan, Lilue Ouyang, Aijia Zunyi Normal Coll Dept Informat Engn Zunyi 563002 Guizhou Peoples R China Zunyi Normal Coll Dept Comp & Informat Sci Zunyi 563002 Guizhou Peoples R China

ISBN: (纸本)9781509040933

For its low efficiency in solving constrained optimization problems, the particle swarm optimization (PSO) is combined with immune algorithm (IA) in this paper. At the same time, an adaptive penalty function formula is designed to propose a hybrid immune PSO (HIPSO) algorithm for finding solution in constrained optimization problems. Through tests of 13 benchmark functions and three engineering optimization examples, it is clear that the performance of the HIPSO algorithm is equal to that of the HPSO algorithm. Whats more, the IA algorithm is not only better than IA algorithm and the PSO algorithm, but also co-evolutionary algorithm and other six kinds of algorithms.

关键词： Immune algorithm particle swarm optimization simplex algorithm hybrid algorithm constrained optimization

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