检索结果-内蒙古大学图书馆

A dynamic particle swarm optimization method applied to global optimizations of engineering inverse problem

COMPEL-THE international JOURNAL FOR computation AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC engineering 2018年第1期37卷 98-117页

作者： Khan, Shafiullah Yang, Shiyou Rehman, Obaid Ur Zhejiang Univ Coll Elect Engn Hangzhou Zhejiang Peoples R China

Purpose - The aim of this paper is to explore the potential of particle swarm optimization (PSO) algorithm to solve an electromagnetic inverse problem. Design/methodology/approach - A modified PSO algorithm is designed. Findings - The modified PSO algorithm is a more stable, robust and efficient global optimizer for solving the well-known benchmark optimization problems. The new mutation approach preserves the diversity of the population, whereas the proposed dynamic and adaptive parameters maintain a good balance between the exploration and exploitation searches. The numerically experimental results of two case studies demonstrate the merits of the proposed algorithm. Originality/value - Some improvements, such as the design of a new global mutation mechanism and introducing a novel strategy for learning and control parameters, are proposed.

关键词： Optimization techniques algorithms Particle swarm optimization Global optimization Inverse problems Dynamic inertia weight Mutation operator Search process

来源：评论

学校读者我要写书评

暂无评论

Cloud Instance Management and Resource Prediction For computation-as-a-Service Platforms 4

Cloud Instance Management and Resource Prediction For Comput...

引用

IEEE international Conference on Cloud engineering (IC2E)

作者： Doyle, Joseph Giotsas, Vasileios Anam, Mohammad Ashraful Andreopoulos, Yiannis Dithen London England

ISBN: (纸本)9781509019618

computation-as-a-Service (CaaS) offerings have gained traction in the last few years due to their effectiveness in balancing between the scalability of Software-as-a-Service and the customisation possibilities of Infrastructure-as-a-Service platforms. To function effectively, a CaaS platform must have three key properties: (i) reactive assignment of individual processing tasks to available cloud instances (compute units) according to availability and predetermined time-to-completion (TTC) constraints;(ii) accurate resource prediction;(iii) efficient control of the number of cloud instances servicing workloads, in order to optimize between completing workloads in a timely fashion and reducing resource utilization costs. In this paper, we propose three approaches that satisfy these properties (respectively): (i) a service rate allocation mechanism based on proportional fairness and TTC constraints;(ii) Kalman-filter estimates for resource prediction;and (iii) the use of additive increase multiplicative decrease (AIMD) algorithms (famous for being the resource management in the transport control protocol) for the control of the number of compute units servicing workloads. The integration of our three proposals into a single CaaS platform is shown to provide for more than 27% reduction in Amazon EC2 spot instance cost against methods based on reactive resource prediction and 38% to 60% reduction of the billing cost against the current state-of-the-art in CaaS platforms (Amazon Lambda and Autoscale).

关键词： computation-as-a-service big data multimedia computing Amazon EC2 spot instances

来源：评论

学校读者我要写书评

暂无评论

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

来源：评论

学校读者我要写书评

暂无评论

Long-term orbit propagation using symplectic integration algorithms 67

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

Comparison of differential grouping and random grouping meth...

引用

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)

来源：评论

学校读者我要写书评

暂无评论

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

来源：评论

学校读者我要写书评

暂无评论

A Study on Prediction of Rainfall using datamining technique

A Study on Prediction of Rainfall using datamining technique

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：