检索结果-内蒙古大学图书馆

Metamodel-assisted distributed genetic algorithms applied to structural shape optimization problems

ENGINEERING OPTIMIZATION 2007年第7期39卷 757-772页

作者： Annicchiarico, W. Cent Univ Venezuela Fac Ingn Inst Mat & Modelos Estruct Caracas 1050A Venezuela

This article is concerned with the development of a general optimization tool based on distributed real genetic algorithms ( DRGAs) assisted by metamodel evaluation and applied to structural shape optimization problems of general boundary- element models ( BEMs). The evaluation fitness function is performed by a surrogate function based on multidimensional Gaussian random field models ( MGRFMs) in order to minimize the computational cost of the evolutionary algorithms. The model boundary of a structural system or a mechanical tool is discretized using the BEM, and selected parts of the boundary are modelled using beta-spline curves or surfaces in order to facilitate re-meshing and adaptation of the boundary to the external actions. Then a hypercube topology of populations of these models follows a genetic evolution process to determine the optimum shape of the system. The optimum models have minimum weight and satisfy all imposed constraints. A numerical example is presented and discussed in order to show the efficiency and robustness of the developed computational tool. The number of function evaluations is substantially reduced compared with previous versions of the optimization algorithm without the metamodel evaluation technique.

关键词： metamodel evaluation distributed genetic algorithms shape optimization

来源：评论

学校读者我要写书评

暂无评论

Effective distributed genetic algorithms for Optimizing Social Utility

Effective Distributed Genetic Algorithms for Optimizing Soci...

引用

13th IEEE International Conference on Commerce and Enterprise Computing (CEC)

作者： Mizutani, Nobuyasu Fujita, Katsuhide Ito, Takayuki Nagoya Inst Technol Comp Sci & Engn Nagoya Aichi Japan Nagoya Inst Technol Sch Technobusiness Adm Nagoya Aichi Japan

ISBN: (纸本)9780769545356

Most real-world negotiation involves multiple interdependent issues that makes an agent's utility function nonlinear. Traditional negotiation mechanisms, which were designed for linear utilities, do not fare well in nonlinear contexts. One of the main challenges in developing effective nonlinear negotiation protocols is scalability, which can produce excessively high failure rate when there are many issues due to computational intractability. One reasonable approach to reducing computational cost while maintaining quality outcomes is to decompose the utility space into several largely independent sub spaces. In this paper, we propose a new method for decomposing a utility space based on the interdependency of issues and employing the genetic algorithms in each issue-group. In addition, our experimental results demonstrate that our method can find higher quality solutions than existing works. They also show that our method is highly effective for reducing the execution time.

关键词： Multi-issue Negotiation distributed genetic algorithms Nonlinear Utility Function

来源：评论

学校读者我要写书评

暂无评论

Gradual distributed real-coded genetic algorithms

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2000年第1期4卷 43-63页

作者： Herrera, F Lozano, M Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain

A major problem in the use of genetic algorithms is premature convergence, a premature stagnation of the search caused by the lack of diversity in the population. One approach for dealing with this problem is the distributed genetic algorithm model. Its basic idea is to keep, in parallell several subpopulations that are processed by genetic algorithms, with each one being independent of the others. Furthermore. a migration mechanism produces a chromosome exchange between the subpopulations. Making distinctions between the subpopulations by applying genetic algorithms with different configurations, ne obtain the so-called heterogeneous distributed genetic algorithms, These algorithms represent a promising mag for introducing a correct exploration/exploitation balance in order to avoid premature convergence and reach approximate final solutions. This paper presents the gradual distributed real-coded genetic algorithms, a type of heterogeneous distributed real-coded genetic algorithms that apply a different crossover operator to each subpopulation, The importance of this operator on the genetic algorithm's performance allowed us to differentiate between the subpopulations in this fashion. Using crossover operators presented for real-coded genetic algorithms, we implement three instances of gradual distributed real-coded genetic algorithms. Experimental results show that the proposals consistently outperform sequential real-coded genetic algorithms and homogeneous distributed real-coded genetic algorithms, which are equivalent to them and other mechanisms presented in the literature. These proposals offer two important advantages at the same time: better reliability and accuracy.

关键词： crossover operator distributed genetic algorithms multiresolution premature convergence selective pressure

来源：评论

学校读者我要写书评

暂无评论

Application of the distributed genetic algorithm for in-core fuel optimization problems under parallel computational environment

引用

JOURNAL OF NUCLEAR SCIENCE AND TECHNOLOGY 2002年第12期39卷 1281-1288页

作者： Yamamoto, A Hashimoto, H Nucl Fuel Ind Ltd Kumatori Osaka 5900481 Japan Japan Res Inst Ltd Nishi Ku Osaka 5500013 Japan

The distributed genetic algorithm (DGA) is applied for loading pattern optimization problems of the pressurized water reactors. A basic concept of DGA follows that of the conventional genetic algorithm (GA). However, DGA equally distributes candidates of solutions (i.e. loading patterns) to several independent "islands" and evolves them in each island. Communications between islands, i.e. migrations of some candidates between islands are performed with a certain period. Since candidates of solutions independently evolve in each island while accepting different genes of migrants, premature convergence in the conventional GA can be prevented. Because many candidate loading patterns should be evaluated in GA or DGA, the parallelization is efficient to reduce turn around time. Parallel efficiency of DGA was measured using our optimization code and good efficiency was attained even in a heterogeneous cluster environment due to dynamic distribution of the calculation load. The optimization code is based on the client/server architecture with the TCP/IP native socket and a client (optimization) module and calculation server modules communicate the objects of loading patterns each other. Throughout the sensitivity study on optimization parameters of DGA, a suitable set of the parameters for a test problem was identified. Finally, optimization capability of DGA and the conventional GA was compared in the test problem and DGA provided better optimization results than the conventional GA.

关键词： distributed genetic algorithms genetic algorithms loading pattern optimization in-core fuel management parallel computing control system

来源：评论

学校读者我要写书评

暂无评论

UNSUPERVISED IMAGE SEGMENTATION USING A distributed genetic ALGORITHM

引用

PATTERN RECOGNITION 1994年第5期27卷 659-673页

作者： ANDREY, P TARROUX, P UNIV PARIS 07 ECOLE NORMALE SUPERDEPT BIOLBIOCHIM & PHYSIOL DEV LABBIOINFORMAT GRPCNRSF-75230 PARIS 05FRANCE

A new methodological approach to digital image processing applied to the particular case of gray-level image segmentation is introduced. The method is based on a modified and simplified version of classifier systems. The labeling function is implemented as a spatially structured set of binary-coded production rules. The labeling is iteratively modified using a distributed genetic algorithm. Results are presented which illustrate both the mechanisms underlying the functioning of the method and its performance on natural images. The relationships between this approach and other related techniques are discussed and it is shown that it compares favorably with these.

关键词： DIGITAL IMAGE PROCESSING CLASSIFIER SYSTEMS distributed genetic algorithms UNSUPERVISED SEGMENTATION CLUSTERING

来源：评论

学校读者我要写书评

暂无评论

An ISODATA clustering procedure for symbolic objects using a distributed genetic algorithm

引用

PATTERN RECOGNITION LETTERS 1999年第7期20卷 659-666页

作者： Ravi, TV Gowda, KC Indian Inst Technol IBM Solut Res Ctr New Delhi 110016 India SJ Coll Engn Dept Comp Sci & Engn Mysore 570006 Karnataka India

A novel ISODATA clustering procedure for symbolic objects is presented using distributed genetic algorithms where in a structured organisation in the distribution of the population is introduced and selection and mating are made within locally distributed subgroups of individuals rather than the whole population. (C) 1999 Elsevier Science B.V. All rights reserved.

关键词： symbolic clustering symbolic similarity genetic algorithms distributed genetic algorithms ISODATA

来源：评论

学校读者我要写书评

暂无评论

REGAL-TC: a distributed genetic algorithm for concept learning based on REGAL and the treatment of counterexamples

引用

SOFT COMPUTING 2011年第7期15卷 1389-1403页

作者： Lopez, L. Ignacio Bardallo, Juan M. De Vega, Miguel A. Peregrin, Antonio Univ Huelva Dept Informat Technol Palos De La Fra Huelva Spain

This paper presents a proposal to improve REGAL, a concept learning system based on a distributed genetic algorithm that learns first-order logic multi-modal concept descriptions in the field of classification tasks. This algorithm has been a pioneer system and source of inspiration for others. Studying the philosophy and experimental behaviour of REGAL, we propose some improvements based principally on a new treatment of counterexamples that promote its underlying goodness in order to achieve better performances in accuracy, interpretability and scalability, so that the new system meets the main requirements for classification rules extraction in data mining. The experimental study carried out shows valuable improvements compared with both REGAL and G-Net distributed genetic algorithms and interesting results compared with some state-of-the-art representative algorithms in this field.

关键词： Concept learning distributed genetic algorithms Cooperative evolution Multi-modalities

来源：评论

学校读者我要写书评

暂无评论

A distributed chromosome genetic algorithm for bin-packing

引用

ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2005年第4-5期21卷 486-495页

作者： Lewis, JE Ragade, RK Kumar, A Biles, WE Univ Louisville Dept Comp Sci & Engn Louisville KY 40292 USA

genetic algorithms (GAs) are excellent approaches to solving complex problems in optimization with difficult constraints. The classic bin-packing optimization problem has been shown to be a NP-complete problem. The loading multiple parts into the build cylinder of a rapid prototyping machine is a type of a bin-packing optimization problem. There are GA applications that work with variations of the bin-packing problem, such as stock cutting, vehicle loading, air container loading, scheduling, and knapsack problems. These applications are mostly based on one-dimensional or two-dimensional considerations, using very specific assumptions. Ikonen et al. (A genetic algorithm for packing three-dimensional non-convex objects having cavities and holes. In: Proceedings of the Seventh International Conference on genetic algorithms. Michigan State University, July 19-23, 1997.) have developed a GA for rapid prototyping called GARP, which utilizes a three-dimensional chromosome structure for the bin-packing of a Sinterstation 2000s build cylinder. GARP allows the Sinterstation 2000 to be used more productively by designing a packing method for multiple parts. GARP was developed using a sequential GA, so execution time is influenced by the number of parts to be packed. Anticipating greater use of time compression technologies, GARP's execution time needs to be reduced. This paper will detail the development of a distributed chromosome GA to reduce the execution time of GARP. The implementation of this distributed GA will improve the efficiency of GARP, by using multiple CPUs to help solve the problem of bin-packing the build cylinder for the rapid prototyping machine. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： distributed genetic algorithms bin-packing rapid prototyping

来源：评论

学校读者我要写书评

暂无评论

A Parallel distributed genetic Algorithm for the Prize Collecting Steiner Tree Problem

A Parallel Distributed Genetic Algorithm for the Prize Colle...

引用

International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Rojas, Francisco Meza, Federico Univ Talca Sch Engn Camino Los Niches Km 1 S-N Curico Chile

ISBN: (纸本)9781467397957

Combinatorial optimization problems are commonly tackled through the use of metaheuristics aimed to improve the way the search space is explored (diversification) and how promising areas are exploited (intensification), in order to obtain good-quality solutions. We present a distributed genetic algorithm to solve the Prize Collecting Steiner Tree Problem, a classical combinatorial optimization problem. The proposed algorithm improves the quality of the solutions by asynchronously combining distributed populations that evolve in parallel, starting from initial heterogeneous configurations. To show the effectiveness of this approach an empirical study that considers both execution time and solution quality is presented. Results show that better solutions are reached when initial populations combine configurations with high and low fitness.

关键词： distributed genetic algorithms PCST Prize Collecting Steiner Tree Problem Parallel Metaheuristics

来源：评论

学校读者我要写书评

暂无评论

DGA: distributed genetic Algorithm Based Relay Assignment in Cooperative Communication

DGA: Distributed Genetic Algorithm Based Relay Assignment in...

引用

9th International Wireless Communications and Mobile Computing Conference (IWCMC)

作者： An, Ruijian Ji, Yusheng Grad Univ Adv Studies Natl Inst Informat Chiyoda Ku Tokyo 1018430 Japan

ISBN: (纸本)9781467324809

A crucial challenge in the implementation of cooperative protocol is how to assign relay nodes properly. In this paper, we address the relay assignment issue in the wireless networks where the source nodes, destination nodes and relay nodes are randomly distributed in a large area. Without any kind of central controller, we try to solve the relay assignment problem in a distributed manner with the local information. Based on this network model, we developed a distributed genetic Algorithm (DGA) based scheme to pursue a better system capacity compared with other distributed algorithms. We also presented a brief discussion and analysis on the DGA algorithm. At last, extensive simulations show that the DGA outperforms other distributed algorithms with obvious improvement of the system capacity and converges fast.

关键词： Relay assignment Cooperative communication distributed genetic algorithms

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：