检索结果-内蒙古大学图书馆

IEEE-PES Transmission and Distribution Conference and Exhibition - Latin America (T&D-LA)

作者： Vargas, Rommel Romero, Ruben Franco, John F. Sao Paulo State Univ UNESP Dept Elect Engn BR-15385000 Ilha Solteira Brazil Sao Paulo State Univ UNESP Sch Energy Engn BR-19274000 Rosana Brazil

ISBN: (纸本)9781538658444

The optimal reconfiguration of radial Electrical Distribution Systems (EDSs) is a classical optimization problem that deals with the operation of the system and is of great interest to the electricity sector. Although there is a large number of approaches in the specialized literature to solve this problem, the solution of the reconfiguration problem for large-scale EDSs is still difficult. This paper proposes a method to solve the reconfiguration problem of EDSs that is based on the specialized metaheuristic biased random-key genetic algorithm, which showed excellent performance on the solution of complex problems in operational research. Tests carried out using a wellknown EDS demonstrate the efficiency of the proposed method.

关键词： biased random-key genetic algorithm electrical distribution systems power losses reconfiguration

来源：评论

学校读者我要写书评

暂无评论

A biased random-key genetic algorithm for the maximum quasi-clique problem

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2018年第3期271卷 849-865页

作者： Pinto, Bruno Q. Ribeiro, Celso C. Rosseti, Isabel Plastino, Alexandre Inst Fed Educ Ciencia & Tecnol Triangulo Mineiro BR-38411104 Uberlandia MG Brazil Univ Fed Fluminense Inst Comp BR-24210240 Niteroi RJ Brazil

Given a graph G = (V, E) and a threshold gamma is an element of (0, 1 j, the maximum cardinality quasi-clique problem consists in finding a maximum cardinality subset C. of the vertices in V such that the density of the graph induced in G by C* is greater than or equal to the threshold gamma. This problem is NP-hard, since it admits the maximum clique problem as a special case. It has a number of applications in data mining, e.g. in social networks or phone call graphs. In this work, we propose a biased random-key genetic algorithm for solving the maximum cardinality quasi-clique problem. Two alternative decoders are implemented for the biased random-key genetic algorithm and the corresponding algorithm variants are evaluated. Computational results show that the newly proposed approaches improve upon other existing heuristics for this problem in the literature. All input data for the test instances and all detailed numerical results are available from Mendeley. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Metaheuristics biased random-key genetic algorithm Maximum quasi-clique problem Maximum clique problem Graph density

来源：评论

学校读者我要写书评

暂无评论

A biased random-key genetic algorithm for the project scheduling problem with flexible resources

引用

TOP 2018年第2期26卷 283-308页

作者： Almeida, Bernardo F. Correia, Isabel Saldanha-da-Gama, Francisco Univ Lisbon Dept Estat & Invest Operac Ctr Matemat Aplicacoes Fundamentais & Invest Oper Fac Ciencias P-1749016 Lisbon Portugal Univ Nova Lisboa Fac Ciencias & Tecnol Dept Matemat Ctr Matemat & Aplicacoes P-2829516 Caparica Portugal

In this paper, we investigate a resource-constrained project scheduling problem with flexible resources. This is an -hard combinatorial optimization problem that consists of scheduling a set of activities requiring specific resource units of several skills. The goal is to minimize the makespan of the project. We propose a biased random-key genetic algorithm for computing feasible solutions for the referred problem. We study different decoding mechanisms: an already existing method in the literature, a new adapted serial scheduling generation scheme, and a combination of both. The new procedure is tested using a set of benchmark instances of the problem. The results provide strong evidence that the new heuristic is robust and yields high-quality feasible solutions.

关键词： Resource-constrained project scheduling Flexible resources biased random-key genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

A biased random-key genetic algorithm for Regression Test Case Prioritization

A Biased Random-Key Genetic Algorithm for Regression Test Ca...

引用

5th IEEE Latin American Conference on Computational Intelligence (LA-CCI)

作者： Carballo, Pablo Perera, Pablo Rama, Santiago Pedemonte, Martin Univ Republica Inst Computac Montevideo Uruguay

ISBN: (纸本)9781538646267

The Test Case Prioritization Problem (TCPP) is a real-world problem that arises in regression testing. It lies in finding an ordering of the test cases of a test suite, such that test cases ordered first should be run first. The classical approach to solve this problem employing GAs is to use permutational encoding, but it requires specific operators to keep the feasibility of the solutions. The biased random-key genetic algorithm (BRKGA) follows a different philosophy for dealing with permutations, using a string of real numbers and a decoder for computing the permutation. In this paper, we propose a BRKGA for solving the TCPP. The experimental evaluation on eleven instances of seven real-world programs shows that BRKGA is able to outperform two different permutational encoding based GAs (with order and cycle crossover operators), and that it has at least a similar performance than another permutational encoding based GA (with partially-mapped crossover) and a GA from a previous work specially conceived for tackling the TCPP.

关键词： Regression testing Search-based software engineering biased random-key genetic algorithm Test case prioritization problem

来源：评论

学校读者我要写书评

暂无评论

A biased random-key genetic algorithm for the container pre-marshalling problem

引用

COMPUTERS & OPERATIONS RESEARCH 2016年 75卷 83-102页

作者： Hottung, Andre Tierney, Kevin Univ Paderborn Decis Support & Operat Res Lab Warburger Str 100 D-33098 Paderborn Germany

The container pre-marshalling problem (CPMP) is performed at container terminals around the world to re-order containers so that they can be more efficiently transferred through the terminal. We introduce a novel decoder for a biased random-key genetic algorithm (BRKGA) that solves the CPMP. The decoder consists of a construction algorithm that learns how to best apply single and compound containers moves to quickly sort a bay of containers. Our approach finds better solutions than the state-of-the-art method on many instances of the standard pre-marshalling benchmarks in less computational time. Furthermore, we perform a computational analysis of different components of the BRKGA decoder to determine what types of heuristics work best for pre-marshalling problems, as well as conduct a feature space analysis of different pre-marshalling approaches. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Container pre-marshalling Maritime applications biased random-key genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

A biased random-key genetic algorithm for the minimization of open stacks problem

引用

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2016年第1-2期23卷 25-46页

作者： Goncalves, Jose Fernando Resende, Mauricio G. C. Costa, Miguel Dias Univ Porto LIAAD INESC TEC Fac Econ Porto P-4200464 Oporto Portugal AT&T Labs Res Florham Pk NJ 07932 USA Univ Porto Fac Econ Porto P-4200464 Oporto Portugal

This paper describes a biased random-key genetic algorithm (BRKGA) for the minimization of the open stacks problem (MOSP). The MOSP arises in a production system scenario, and consists of determining a sequence of cutting patterns that minimize the maximum number of open stacks during the cutting process. The proposed approach combines a BRKGA and a local search procedure for generating the sequence of cutting patterns. A novel fitness function for evaluating the quality of the solutions is also developed. Computational tests are presented using available instances taken from the literature. The high quality of the solutions obtained validate the proposed approach.

关键词： minimization of open stacks problem cutting pattern biased random-key genetic algorithm random keys

来源：评论

学校读者我要写书评

暂无评论

A Python/C plus plus library for bound-constrained global optimization using a biased random-key genetic algorithm

引用

JOURNAL OF COMBINATORIAL OPTIMIZATION 2015年第3期30卷 710-728页

作者： Silva, R. M. A. Resende, M. G. C. Pardalos, P. M. Univ Fed Pernambuco Ctr Informat CIn Recife PE Brazil AT&T Labs Res Algorithms & Optimizat Res Dept Florham Pk NJ 07932 USA Univ Florida Dept Ind & Syst Engn Gainesville FL 32611 USA

This paper describes libbrkga, a GNU-style dynamic shared Python/C++ library of the biased random-key genetic algorithm (BRKGA) for bound constrained global optimization. BRKGA (J Heuristics 17:487-525, 2011b) is a general search metaheuristic for finding optimal or near-optimal solutions to hard optimization problems. It is derived from the random-key genetic algorithm of Bean (ORSA J Comput 6:154-160, 1994), differing in the way solutions are combined to produce offspring. After a brief introduction to the BRKGA, including a description of the local search procedure used in its decoder, we show how to download, install, configure, and use the library through an illustrative example.

关键词： biased random-key genetic algorithm Global optimization Multimodal functions Continuous optimization Heuristic Stochastic algorithm Stochastic local search Nonlinear programming

来源：评论

学校读者我要写书评

暂无评论

A biased random-key genetic algorithm for the capacitated minimum spanning tree problem

引用

COMPUTERS & OPERATIONS RESEARCH 2015年 57卷 95-108页

作者： Ruiz, Efrain Albareda-Sambola, Maria Fernandez, Elena Resende, Mauricio G. C. Univ Politecn Cataluna Dept Estadist & Invest Operat BarcelonaTech ES-08034 Barcelona Spain Univ Politecn Cataluna Dept Estadist & Invest Operat BarcelonaTech Barcelona 08222 Spain AT&T Labs Res Network Evolut Res Dept Middletown NJ 07748 USA

This paper focuses on the capacitated minimum spanning tree (CMST) problem. Given a central processor and a set of remote terminals with specified demands for traffic that must flow between the central processor and terminals, the goal is to design a minimum cost network to carry this demand. Potential links exist between any pair of terminals and between the central processor and the terminals. Each potential link can be included in the design at a given cost. The CMST problem is to design a minimum-cost network connecting the terminals with the central processor so that the flow on any arc of the network is at most Q. A biased random-key genetic algorithm (BRKGA) is a metaheuristic for combinatorial optimization which evolves a population of random vectors that encode solutions to the combinatorial optimization problem. This paper explores several solution encodings as well as different strategies for some steps of the algorithm and finally proposes a BRKGA heuristic for the CMST problem. Computational experiments are presented showing the effectiveness of the approach: Seven new best-known solutions are presented for the set of benchmark instances used in the experiments. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Optimization Combinatorial optimization Networks Graphs Trees Spanning trees Capacitated minimum spanning tree Heuristics biased random-key genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

A biased random-key genetic algorithm for Placement of Virtual Machines across Geo-Separated Data Centers 15

A Biased Random-key Genetic Algorithm for Placement of Virtu...

引用

17th genetic and Evolutionary Computation Conference (GECCO)

作者： Stefanello, Fernando Aggarwal, Vaneet Buriol, Luciana S. Goncalves, Jose F. Resende, Mauricio G. C. Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil Purdue Univ Sch Ind Engn W Lafayette IN 47907 USA Univ Porto Fac Econ P-4200464 Oporto Portugal Amazon Com Math Optimizat & Planning Seattle WA 98109 USA

ISBN: (纸本)9781450334723

Cloud computing has recently emerged as a new technology for hosting and supplying services over the Internet. This technology has brought many benefits, such as eliminating the need for maintaining expensive computing hardware and allowing business owners to start from small and increase resources only when there is a rise in service demand. With an increasing demand for cloud computing, providing performance guarantees for applications that run over cloud become important. Applications can be abstracted into a set of virtual machines with certain guarantees depicting the quality of service of the application. In this paper, we consider the placement of these virtual machines across multiple data centers, meeting the quality of service requirements while minimizing the bandwidth cost of the data centers. This problem is a generalization of the NP-hard Generalized Quadratic Assignment Problem (GQAP). We formalize the problem and propose a novel algorithm based on a biased random-key genetic algorithm (BRKGA) to find nearoptimal solutions for the problem. The experimental results show that the proposed algorithm is effective in quickly finding feasible solutions and it produces better results than a baseline aproach provided by a commercial solver and a multi-start algorithm.

关键词： Combinatorial optimization Cloud computing biased random-key genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

An extended Akers graphical method with a biased random-key genetic algorithm for job-shop scheduling

引用

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2014年第2期21卷 215-246页

作者： Goncalves, Jose Fernando Resende, Mauricio G. C. Univ Porto Fac Econ INESC TEC LIAAD P-4200464 Oporto Portugal AT&T Labs Res Algorithms & Optimizat Res Dept Florham Pk NJ 07932 USA

This paper presents a local search, based on a new neighborhood for the job-shop scheduling problem, and its application within a biased random-key genetic algorithm. Schedules are constructed by decoding the chromosome supplied by the genetic algorithm with a procedure that generates active schedules. After an initial schedule is obtained, a local search heuristic, based on an extension of the 1956 graphical method of Akers, is applied to improve the solution. The new heuristic is tested on a set of 205 standard instances taken from the job-shop scheduling literature and compared with results obtained by other approaches. The new algorithm improved the best-known solution values for 57 instances.

关键词： job-shop scheduling genetic algorithm biased random-key genetic algorithm heuristics random keys graphical approach

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：