检索结果-内蒙古大学图书馆

Network-aware virtual machine migration in an overcommitted cloud

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2017年 76卷 428-442页

作者： Zhang, Weizhe Han, Shuo He, Hui Chen, Huixiang Harbin Inst Technol Sch Comp Sci & Technol Box 320 Harbin 150001 Heilongjiang Peoples R China

Virtualization, which acts as the underlying technology for cloud computing, enables large amounts of third-party applications to be packed into virtual machines (VMs). VM migration enables servers to be reconsolidated or reshuffled to reduce the operational costs of data centers. The network traffic costs for VM migration currently attract limited attention. However, traffic and bandwidth demands among VMs in a data center account for considerable total traffic. VM migration also causes additional data transfer overhead, which would also increase the network cost of the data center. This study considers a network-aware VM migration (NetVMM) problem in an overcommitted cloud and formulates it into a non-deterministic polynomial time-complete problem. This study aims to minimize network traffic costs by considering the inherent dependencies among VMs that comprise a multi-tier application and the underlying topology of physical machines and to ensure a good trade-off between network communication and VM migration costs. The mechanism that the swarm intelligence algorithm aims to find is an approximate optimal solution through repeated iterations to make it a good solution for the VM migration problem. In this study, genetic, algorithm (GA) and artificial bee colony (ABC) are adopted and changed to suit the VM migration problem to minimize the network cost. Experimental results show that GA has low network costs when VM instances are small. However, when the problem size increases, ABC is advantageous to GA. The running time of ABC is also nearly half than that of GA. To the best of our knowledge, we are the first to use ABC to solve the NetVMM problem. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Cloud computing Network-aware virtual machine migration Network communication costs Migration costs Genetic algorithm artificial bee colony algorithm

来源：评论

学校读者我要写书评

暂无评论

Two grouping-based metaheuristics for clique partitioning problem

引用

APPLIED INTELLIGENCE 2017年第2期47卷 430-442页

作者： Sundar, Shyam Singh, Alok Natl Inst Technol Raipur Dept Comp Applicat Raipur 492010 Madhya Pradesh India Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 Andhra Pradesh India

Given a connected, undirected graph G = (V,E), where V is the set of vertices and E is the set of edges, the clique partitioning problem (CPP) seeks on this graph a partition of set V into minimum number of subsets such that each subset is a clique. The CPP is an -Hard problem and finds numerous practical applications in diverse domains like digital design synthesis, clustering etc. Despite its computational complexity and its numerous applications, only problem-specific heuristics have been developed so far for this problem in the literature. In this paper, two metaheuristic techniques - a steady-state grouping genetic algorithm and an artificial bee colony algorithm - are proposed for the CPP. Both the proposed approaches are designed in such a way that the grouping structure of the CPP is exploited effectively while generating new solutions. Since artificial bee colony algorithm is comparatively a new metaheuristic technique, special attention has been given to the design of this algorithm for the CPP and we came out with a new neighboring solution generation method utilizing solution components from multiple solutions. The proposed approaches have been tested on publicly available 37 DIMACS graph instances. Computational results show the effectiveness of the proposed approaches.

关键词： Clique partitioning problem Grouping problem Steady-state grouping genetic algorithm artificial bee colony algorithm

来源：评论

学校读者我要写书评

暂无评论

artificial bee colony algorithm for Transient Performance Augmentation of Grid Connected Distributed Generation

引用

1st International Conference on Swarm, Evolutionary, and Memetic Computing

作者： Chatterjee, A. Ghoshal, S. P. Mukherjee, V. Asansol Engn Coll Dept Elect Engn Asansol W Bengal India Natl Inst Technol Dept Elect Engn Durgapur W Bengal India Indian Sch Mines Dept Elect Engn Dhanbad India

ISBN: (纸本)9783642175626

In this paper, a conventional thermal power system equipped with automatic voltage regulator, IEEE type dual input power system stabilizer (PSS) PSS3B and integral controlled automatic generation control loop is considered. A distributed generation (DG) system consisting of aqua electrolyzer, photovoltaic cells, diesel engine generator, and some other energy storage devices like flywheel energy storage system and battery energy storage system is modeled. This hybrid distributed system is connected to the grid. While integrating this DG with the onventional thermal power system, improved transient performance is noticed. Further improvement in the transient performance of this grid connected DG is observed with the usage of superconducting magnetic energy storage device. The different tunable parameters of the proposed hybrid power system model are optimized by artificial bee colony (ABC) algorithm. The optimal solutions offered by the ABC algorithm are compared with those offered by genetic algorithm (GA). It is also revealed that the optimizing performance of the ABC is better than the GA for this specific application.

关键词： artificial bee colony algorithm distributed generation energy storage devices genetic algorithm superconducting magnetic energy storage device

来源：评论

学校读者我要写书评

暂无评论

A new meta-heuristic butterfly-inspired algorithm

引用

JOURNAL OF COMPUTATIONAL SCIENCE 2017年 23卷 226-239页

作者： Qi, Xiangbo Zhu, Yunlong Zhang, Hao Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Liaoning Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

This paper proposes a novel bio-inspired algorithm named artificial Butterfly Optimization (ABO) algorithm. The new algorithm is based on the mate-finding strategy of some butterfly species. Two groups of artificial butterflies are employed for simulating the flight strategies. If the flight strategies of artificial butterflies are redefined, ABO can develop a new algorithm. From this point, ABO is a mimic-life algorithm in grandness. By presenting three flight strategies, we build two new algorithms named ABO1 and ABO2. We validate the two new algorithms and compare their performance with other well-known nature-inspired algorithms on twenty-two benchmark functions. The experimental results show that the proposed algorithm is able to provide very proihising and competitive results on most benchmark functions. It also proves that the ABO algorithm provides a new effective computational framework for solving optimization problems. (C) 2017 Elsevier B.V. All rights reserved.

关键词： artificial butterfly optimization artificial bee colony algorithm Particle swarm optimization Genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

A comparative study on swarm intelligence for structure learning of Bayesian networks

引用

SOFT COMPUTING 2017年第22期21卷 6713-6738页

作者： Ji, Junzhong Yang, Cuicui Liu, Jiming Liu, Jinduo Yin, Baocai Beijing Univ Technol Coll Comp Sci & Technol Beijing Municipal Key Lab Multimedia & Intelligen Beijing Peoples R China Hong Kong Baptist Univ Dept Comp Sci & Technol Kowloon Hong Kong Peoples R China

A Bayesian network (BN) is an important probabilistic model in the field of artificial intelligence and a powerful formalism used to describe uncertainty in the real world. As science and technology develop, considerable data on complex systems have been acquired by various means, which presents a significant challenge regarding how to accurately and robustly learn a network structure for a complex system. To address this challenge, many BN structure learning methods based on swarm intelligence have been developed. In this study, we perform a systematic comparison of three typical methods based on ant colony optimization, artificial bee colony algorithm, and bacterial foraging optimization. First, we analyze and summarize their main characteristics from the perspective of stochastic searching. Second, we conduct thorough experimental comparisons to examine the roles of different mechanisms in each method by means of multiaspect metrics, i.e., the K2 score, structural differences, and execution time. Next, we perform further experiments to validate the robustness of different algorithms on some benchmark data sets with noise. Finally, we present the prospects and references for researchers who are engaged in learning BN networks.

关键词： Bayesian network structure learning Swarm intelligence Ant colony optimization artificial bee colony algorithm Bacterial foraging optimization

来源：评论

学校读者我要写书评

暂无评论

Clustering performance comparison of new generation meta-heuristic algorithms

引用

KNOWLEDGE-BASED SYSTEMS 2017年 130卷 1-16页

作者： Ozbakir, Lale Turna, Fatma Erciyes Univ Ind Engn Dept TR-38039 Kayseri Turkey

This article addressed two new generation meta-heuristic algorithms that are introduced to the literature recently. These algorithms, proved their performance by benchmark standard test functions, are implemented to solve clustering problems. One of these algorithms called Ions Motion Optimization and it is established from the motions of ions in nature. The other algorithm is Weighted Superposition Attraction and it is predicated on two fundamental principles, which are "attracted movements of agents" and "superposition". Both of the algorithms are applied to different benchmark data sets consisted of continuous, categorical and mixed variables, and their performances are compared to Particle Swarm Optimization and artificial bee colony algorithms. To eliminate the infeasible solutions, Deb's rule is integrated into the algorithms. The comparison results indicated that both of the algorithms, Ions Motion Optimization and Weighted Superposition Attraction, are competitive solution approaches for clustering problems. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Cluster analysis Ions motion algorithm Weighted superposition attraction algorithm Particle swarm optimization algorithm artificial bee colony algorithm

来源：评论

学校读者我要写书评

暂无评论

Applying Optimized Support Vector Regression Models for Prediction of Tunnel Boring Machine Performance

引用

GEOTECHNICAL AND GEOLOGICAL ENGINEERING 2017年第5期35卷 2205-2217页

作者： Fattahi, Hadi Babanouri, Nima Arak Univ Technol Dept Min Engn Arak Iran Hamedan Univ Technol Dept Min Engn Hamadan Iran

One of the main factors in the effective application of a tunnel boring machine (TBM) is the ability to accurately estimate the machine performance in order to determine the project costs and schedule. Predicting the TBM performance is a nonlinear and multivariable complex problem. The aim of this study is to predict the performance of TBM using the hybrid of support vector regression (SVR) and the differential evolution algorithm (DE), artificial bee colony algorithm (ABC), and gravitational search algorithm (GSA). The DE, ABC and GSA are combined with the SVR for determining the optimal value of its user defined parameters. The optimization implementation by the DE, ABC and GSA significantly improves the generalization ability of the SVR. The uniaxial compressive strength (UCS), average distance between planes of weakness (DPW), the angle between tunnel axis and the planes of weakness (alpha), and intact rock brittleness (BI) were considered as the input parameters, while the rate of penetration was the output parameter. The prediction models were applied to the available data given in the literature, and their performance was assessed based on statistical criteria. The results clearly show the superiority of DE when integrated with SVR for optimizing values of its parameters. In addition, the suggested model was compared with the methods previously presented for predicting the TBM penetration rate. The comparative results revealed that the hybrid of DE and SVR yields a robust model which outperforms other models in terms of the higher correlation coefficient and lower mean squared error.

关键词： Tunnel boring machine Penetration rate Support vector regression Differential evolution algorithm artificial bee colony algorithm Gravitational search algorithm

来源：评论

学校读者我要写书评

暂无评论

An Improved Collaborative Filtering Based Recommender System using Bat algorithm

引用

Procedia Computer Science 2018年 132卷 1795-1803页

作者： Sambhav Yadav Vikesh Shreyam Sushama Nagpal Netaji Subhas Institute of Technology Sector-3 Dwarka Delhi 110078 India

Recommender Systems have proven to be of great aid in dealing with the issue of Information Overload by improving the user experience through quality recommendations. In recent times, heuristic techniques have been employed by researchers in recommender systems along with traditional methods of collaborative and content based filtering. On the same account, in this work a Bat algorithm based heuristic technique has been used to compute the weights of items (features) so as to find better neighbourhood for the active user. We argue and also prove using the results that this technique of giving weights to items using heuristic methods helps in achieving better personalized recommendations. The performance of this system was also compared to that of artificial bee colony based system (ABC). The results indicated that BA performed 6.9% better than ABC in terms of Mean Absolute Error and F1 Score using our technique.

关键词： Bat algorithm Recommender System artificial bee colony algorithm Swarm Intelligence Collaborative Filtering

来源：评论

学校读者我要写书评

暂无评论

An Approach to Web Services Selection for Multiple Users

引用

IEEE ACCESS 2017年 5卷 15093-15104页

作者： Zhu, Wenlong Yin, Beibei Gong, Siqian Cai, Kai-Yuan Beihang Univ Sch Automat Sci & Elect Engn Beijing 100083 Peoples R China

Web service selection for multiple users is an important aspect for achieving efficient operations for web service applications. Its aim is to select optimal solutions, in which each abstract web service in the workflow of a web application is bound to its corresponding concrete web service with the optimal quality of service (QoS), for all users based on their QoS requirements for the workflow. There are a lot of approaches to resolve this problem, but they do not consider each user's different QoS requirements or have prohibitively large overhead for using these approaches. In this paper, we present an approach to significantly improve the efficiency of web service selection by the advanced a-fully polynomial time approximation scheme to calculate the Pareto optimal set, where each solution is not dominated by others. Additionally, this approach reduces its overhead further by adopting artificial bee colony algorithm to select an optimal solution from the Pareto set for each user. Experimental results are presented to show the efficiency of this approach.

关键词： Advanced a-fully polynomial time approximation scheme artificial bee colony algorithm multiple users Pareto optimal set web service selection

来源：评论

学校读者我要写书评

暂无评论

An Effective Content Based Medical Image Retrieval by Using ABC based artificial Neural Network (ANN)

引用

CURRENT MEDICAL IMAGING REVIEWS 2017年第3期13卷 223-230页

作者： Sankar, S. Perumal Vishwanath, N. Lang, Hong Jer Karthick, S. Toc H Inst Technol Dept Elect & Commun Engn Ernakulam India Toc H Inst Technol Dept Comp Sci & Engn Ernakulam India Taylors Univ Sch Comp & IT Subang Jaya Malaysia Sea Sense Softwares Pvt Ltd Kanyakumari Tamil Nadu India

Background: Content Based Image Retrieval (CBIR) has always been a demanding research area as it involves searching of digital images from the collection of images. Difficulty exists in retrieving images for the query posed by concentrating on the factors;computational complexity and accuracy. Though many research works span around CBIR, Content Based Medical Image Retrieval (CBMIR) plays a vital role in the area of medical diagnosis. Methods: This paper introduces an enhanced feature extraction and retrieval phase for MRI images using Edge based GLCM (EGLCM), and artificial bee colony (ABC) based artificial Neural Network (ANN) to generate the best results and to yield a higher retrieval accuracy. Discussion: Our proposed work has four important phases namely Pre-Processing, Feature Extraction, Optimized Retrieval using ABC based ANN. and GLCM. GLCM generates the co-occurrence matrix to calculate Homogeneity, Energy, Correlation, and Contrast as texture features. As a result, eight features act as a feature vector to depict images. Then, to optimize the retrieval task, conjunction with ABC based ANN uses Association rule mining with their significant features in which we carry out Training and Validation phases in 1000 and 100 MRI images. Conclusion: The performance of the method envisaged in this paper is encouraging, and the method is cost effective as compared to the methods described in the literature, based on the performance metrics namely precision, accuracy and recall. A comparative graph is given to corroborate the same.

关键词： artificial bee colony algorithm artificial neural network (ANN) content based image retrieval (CBIR) content based medical image retrieval (CBMIR) grey level co-occurrence matrix (GLCM) local gabor XOR pattern

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：