检索结果-内蒙古大学图书馆

distributed genetic algorithm for optimal planar arrays of aperture synthesis telescope

Journal of Systems Engineering and Electronics 2004年第3期15卷 419-425页

作者：贺小箭唐新怀尤晋元文建国 DepartmentofComputerScienceandEngineering ShanghaiJiaotongUniversityShanghai200030P.R.China ChinaUniversityofMiningandTechnology Beijing100083P.R.China

Sparse arrays of telescopes have a limited (u, v)-plane coverage. In this paper, an optimization method for designing planar arrays of an aperture synthesis telescope is proposed that is based on distributed genetic algorithm. This distributed genetic algorithm is implemented on a network of workstations using community communication model. Such an aperture synthesis system performs with imperfection of (u, v) components caused by deviations and(or) some missing baselines. With the maximum (u, v)-plane coverage of this rotation-optimized array, the image of the source reconstructed by inverse Fourier transform is satisfactory.

关键词： distributed genetic algorithm optical aperture synthesis optimum planar array (u v) -spectrum sampling.

来源：评论

学校读者我要写书评

暂无评论

distributed genetic algorithm for application placement in the compute continuum leveraging infrastructure nodes for optimization

引用

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 160卷 154-170页

作者： Guerrero, Carlos Lera, Isaac Juiz, Carlos Univ Balear Isl Comp Sci Dept Crta Valldemossa Km 7-5 E-07121 Palma De Mallorca Spain

The increasing complexity of Compute Continuum environments calls for efficient resource optimization techniques. In this paper, we propose and evaluate three distributed designs of a genetic algorithm (GA) for resource optimization, within an increasing degree of distribution. The designs leverage the execution of the GA in the infrastructure devices themselves by dealing with the specific features of this domain: constrained resources and wide geographical distribution of the devices. For their evaluation, we implemented a benchmark case using the NSGA-II for the specific problem of optimizing the application placement, according to the guidelines of our three distributed designs. These three experimental scenarios were compared against a control case, representing a traditional centralized version of this GA algorithm, evaluating solution quality and network overhead. The results show that the design with the lowest distribution degree, which keeps centralized storage of the objective space, achieves comparable solution quality to the traditional approach but incurs a higher network load. The second design, which completely distributes the population between the workers, reduces network overhead but exhibits lower solution diversity while keeping enough good results in terms of optimization objective minimization. The second design demonstrates the highest overall efficiency in optimization performance and network cost. Finally, the proposal with a distributed population that only interchanges solutions between the workers' neighbors achieves the lowest network load but with compromised solution quality.

关键词： Cloud-edge continuum Fog computing distributed genetic algorithm Resource optimization Multi-objective optimization Application placement

来源：评论

学校读者我要写书评

暂无评论

distributed genetic algorithm for energy-efficient resource management in sensor networks 06

Distributed genetic algorithm for energy-efficient resource ...

引用

8th Annual genetic and Evolutionary Computation Conference

作者： Qiu, Qinru Wu, Qing Burns, Daniel Holzhauer, Douglas SUNY Binghamton Dept Elect & Comp Engn Binghamton NY 13902 USA Air Force Res Lab Rome NY 13441 USA

ISBN: (纸本)9781595931863

In this work we consider energy-efficient resource management in an environment monitoring and hazard detection sensor network. Our goal is to allocate different detection methods to different sensor nodes in the way such that the required detection probability can be achieved while the network lifetime is maximized. The optimization algorithm is designed based on the Island multi-deme genetic algorithm (GA). The experimental results show that our algorithm increases the network lifetime by approximately 14.4% in average compared with the heuristic approaches. We also investigate the effect of the configuration parameters on the searching quality of the proposed distributed GA. A regression model is derived empirically that estimates the runtime of the distributed GA given the configuration parameters such as the subpopulation size, parallelism, and migration rate. Once the model has been fit to a group of data, it can be utilized to find the efficient configurations of the proposed algorithm.

关键词： distributed genetic algorithm sensor network energy aware design resource management

来源：评论

学校读者我要写书评

暂无评论

distributed genetic algorithm on Cluster of Intel Xeon Phi Co-processors 1

引用

5th International Conference on Future Data and Security Engineering (FDSE)

作者： Nguyen Quang-Hung Anh-Tu Ngoc Tran Nam Thoai Vietnam Natl Univ Ho Chi Minh City Ho Chi Minh City Univ Technol Fac Comp Sci & Engn Ho Chi Minh City Vietnam

ISBN: (数字)9783030031923

ISBN: (纸本)9783030031923;9783030031916

This paper presents a study to parallelize genetic algorithm on cluster of Intel Xeon Phi co-processors. Our study investigates using hybrid MPI and OpenMP to parallelize genetic algorithm (GA) onto high performance computing (HPC) servers included Intel Xeon Phi co-processors. We will revise sequential GA and parallelize the GA into parallel GA, which a highly portable, modern code and optimized in any MIC architectures like Knights Corner (KNC), Knights Landing (KNL), or even the up-coming processors of Intel like Knights Hill by using Intel compiler, MPI and several OpenMP directives. Our implementation is experimented on built-in 50-TFlops Supernode-XP, which is cluster of HPC servers (each server has two Intel Xeon Phi cards). Our simulated result shows that the proposed distributed GA onto cluster of Intel Xeon Phi co-processors (MICs) reduces significant execution time in comparison with sequential GA.

关键词： distributed genetic algorithm Intel Xeon Phi MIC

来源：评论

学校读者我要写书评

暂无评论

A parallel and distributed genetic algorithm on loosely-coupled multiprocessor systems

引用

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 1998年第4期E81A卷 540-546页

作者： Matsumura, T Nakamura, M Okech, J Onaga, K Univ Ryukyus Dept Informat Engn Okinawa 9030213 Japan Jomo Kenyatta Univ Agr & Technol Dept Math & Comp Sci Nairobi Kenya

In this paper we consider a parallel and distributed computation of generic algorithms on loosely-coupled multiprocessor systems. Loosely-coupled ones are more suitable for massively parallel processing and also more easily VLSI implementation than rightly-coupled ones. However, communication overhead on parallel processing is more serious for loosely-coupled ones. We propose in this paper a parallel and distributed execution method of genetic algorithm on loosely-coupled multiprocessor systems of fixed network topologies in which each processor element carries out genetic operations on its own chromosome set and communicates with only the neighbors in order to save communication overhead. We evaluate the proposed method on the multiprocessor systems with ring, torus, and hypercube topologies for benchmark problem instances. From the results, we find that the ring topology is more suitable for the proposed parallel and distributed execution since variety of chromosomes in the ring is kept much more than that in the others. Moreover, we also propose a new network topology called cane which is a hierarchical connection of ring topologies. We show its effectiveness by experimental evaluation.

关键词： parallel genetic algorithm distributed genetic algorithm loosely-coupled multiprocessor system network topology cone topology

来源：评论

学校读者我要写书评

暂无评论

A Driving Behavior Awareness Model based on a Dynamic Bayesian Network and distributed genetic algorithm

引用

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2018年第1期11卷 469-482页

作者： Xie, Guotao Gao, Hongbo Huang, Bin Qian, Lijun Wang, Jianqiang Hefei Univ Technol Dept Automot Engn Hefei 230009 Anhui Peoples R China Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China Collaborat Innovat Ctr Elect Vehicles Beijing Beijing 100084 Peoples R China

It is necessary for automated vehicles (AVs) and advanced driver assistance systems (ADASs) to have a better understanding of the traffic environment including driving behaviors. This study aims to build a driving behavior awareness (DBA) model that can infer driving behaviors such as lane change. In this study, a dynamic Bayesian network DBA model is proposed, which includes three layers, namely, the observation, hidden and behavior layer. To enhance the performance of the DBA model, the network structure is optimized by employing a distributed genetic algorithm (GA). Using naturalistic driving data in Beijing, the comparison between the optimized model and other non-optimized models such as the hidden Markov model (HMM) and HMM with a mixture of Gaussian outputs (GM-HMM) indicates that the optimized model could estimate driving behaviors earlier and more accurately.

关键词： Automated Vehicle Advanced Driver Assistance System Driving Behavior Awareness Dynamic Bayesian Network distributed genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimizing parameters of a motion detection system by means of a distributed genetic algorithm

引用

IMAGE AND VISION COMPUTING 2005年第9期23卷 815-829页

作者： Bevilacqua, A Univ Bologna DEIS ARCES Dept Elect Comp Sci & Syst I-40136 Bologna Italy

The main task of traffic monitoring applications is to identify moving targets. Usually, these applications require that a large number of parameters is tuned in order to work properly. In the motion detection system we have developed, about thirty parameters have been required to be optimized. This paper shows how a distributed implementation of a genetic algorithm (GA) over a network of workstations can successfully accomplish the parameter optimization task within a reduced amount of time. Accurate experiments accomplished on a challenging training sequence yield optimal parameter values. Four more test sequences allow us to assess the generality of the results previously attained. (c) 2005 Elsevier B.V. All rights reserved.

关键词： motion detection parameter optimization traffic monitoring visual surveillance distributed genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Enhancing the curvature mode shape method for structural damage severity estimation by means of the distributed genetic algorithm

引用

ENGINEERING OPTIMIZATION 2021年第4期53卷 683-701页

作者： Bagherkhani, Ali Baghlani, Abdolhossein Shiraz Univ Technol Dept Civil & Environm Engn Shiraz Iran

An important challenge in damage detection based on the curvature mode shape (CMS) method is recognizing damage severity in damaged structures. Here, an efficient approach is proposed based on the finite element (FE) method, which detects the existence, location and severity of damage using the distributed genetic algorithm in conjunction with CMS. First, using the mode shape of the structure, curvature is calculated at the nodes of the structure in the FE model. By comparing the curvature between the intact and damaged structures, the existence and location of damage are determined. Next, by defining an optimization problem, damage severity in the damaged elements is determined. Results of applying the proposed approach on beam and frame structures under various damage scenarios and an experimental set-up show that this method is very effective in damage detection of such structures. Furthermore, the performance and accuracy of the proposed method are examined for various noise levels.

关键词： Damage detection curvature mode shape optimization distributed genetic algorithm finite element method

来源：评论

学校读者我要写书评

暂无评论

Patient assignment optimization in cloud healthcare systems: a distributed genetic algorithm

引用

HEALTH INFORMATION SCIENCE AND SYSTEMS 2023年第1期11卷 30页

作者： Pang, Xinyu Ge, Yong-Feng Wang, Kate Traina, Agma J. M. Wang, Hua Guangdong Technion Israel Inst Technol Shantou Peoples R China Victoria Univ Inst Sustainable Ind & Liveable Cities Melbourne Australia RMIT Univ Sch Hlth & Biomed Sci Melbourne Australia Univ Sao Paulo Inst Math & Comp Sci Sao Paulo Brazil

Integrating Internet technologies with traditional healthcare systems has enabled the emergence of cloud healthcare systems. These systems aim to optimize the balance between online diagnosis and offline treatment to effectively reduce patients' waiting times and improve the utilization of idle medical resources. In this paper, a distributed genetic algorithm (DGA) is proposed as a means to optimize the balance of patient assignment (PA) in cloud healthcare systems. The proposed DGA utilizes individuals as solutions for the PA optimization problem and generates better solutions through the execution of crossover, mutation, and selection operators. Besides, the distributed framework in the DGA is proposed to improve its population diversity and scalability. Experimental results demonstrate the effectiveness of the proposed DGA in optimizing the PA problem within the cloud healthcare systems.

关键词： Patient assignment distributed genetic algorithm Cloud healthcare system Evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

distributed genetic algorithm:: A case-study of evolution by direct exchange of chromosomes

引用

COMPUTING AND INFORMATICS 2003年第6期22卷 575-596页

作者： Kubík, A Silesian Univ Inst Comp Sci Opava 74601 Czech Republic

It is considered a difficult task to design a controller from scratch for a robot in a dynamic environment. Evolutionary and genetic algorithms are frequently used to find a solution with desired properties. The evolution is supposed to run on one robot or agent processor. In this article eve explore the possibility of dividing the genome among several robots. Robots exchange among each other successful controllers in the form of chromosomes that code for the weight vector in a neural controller. Thus;the evolution can he faster because it runs in a parallel manner over the whole robotic society;not in one robot. Second interesting point is the exchange of experience among robots. Robots can send each other parts of the controllers that they evolved on their own. They can learn behavioral strategies that other robots developed. We describe experiments done with small Khepera robots in the domain of a benchmark test for evolutionary robotics. We compare the behavior generated by one robot. based on its individual evolution with the robot that profits from sharing the full set of chromosomes with the other robot.

关键词： distributed genetic algorithm evolution multi-agent system experience exchange computational cognitivism

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：