检索结果-内蒙古大学图书馆

randomized heuristics for handover minimization in mobility networks

JOURNAL OF HEURISTICS 2013年第6期19卷 845-880页

作者： Moran-Mirabal, L. F. Gonzalez-Velarde, J. L. Resende, M. G. C. Silva, R. M. A. Tecnol Monterrey Qual & Mfg Ctr Monterrey Mexico AT&T Labs Res Algorithms & Optimizat Res Dept Florham Pk NJ 07932 USA Univ Fed Pernambuco Ctr Informat CIn Recife PE Brazil

A mobile device connects to the cell tower (base station) from which it receives the strongest signal. As the device moves it may connect to a series of towers. The process in which the device changes the base station it is connected to is called handover. A cell tower is connected to a radio network controller (RNC) which controls many of its operations, including handover. Each cell tower handles an amount of traffic and each radio network controller has capacity to handle a maximum amount of traffic from all base stations connected to it. Handovers between base stations connected to different RNCs tend to fail more often than handovers between base stations connected to the same RNC. Handover failures result in dropped connections and therefore should be minimized. The Handover Minimization Problem is to assign towers to RNCs such that RNC capacity is not violated and the number of handovers between base stations connected to different RNCs is minimized. We describe an integer programming formulation for the handover minimization problem and show that state-of-the-art integer programming solvers can solve only very small instances of the problem. We propose several randomized heuristics for finding approximate solutions of this problem, including a GRASP with path-relinking for the generalized quadratic assignment problem, a GRASP with evolutionary path-relinking, and a biased random-key genetic algorithm. Computational results are presented.

关键词： Mobility networks Handover minimization randomized heuristics GRASP biased random-key genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Unsupervised feature selection based on bio-inspired approaches

引用

SWARM AND EVOLUTIONARY COMPUTATION 2020年 52卷

作者： Martarelli, Nadia Junqueira Nagano, Marcelo Seido Univ Sao Paulo Sao Carlos Sch Engn 400 Trabalhador Sao Carlense Ave Sao Carlos SP Brazil

In recent years, the scientific community has witnessed an explosion in the use of pattern recognition algorithms. However, little attention has been paid to the tasks preceding the execution of these algorithms, the preprocessing activities. One of these tasks is dimensionality reduction, in which a subset of features that improves the performance of the mining algorithm is located and algorithm's runtime is reduced. Although there are many methods that address the problems in pattern recognition algorithms, effective solutions still need to be researched and explored. Hence, this paper aims to address three of the issues surrounding these algorithms. First, we propose adapting a promising meta-heuristic called biased random-key genetic algorithm, which considers a random initial population construction. We call this algorithm as unsupervised feature selection by biased random-key genetic algorithm I. Next, we propose an approach for building the initial population partly in a deterministic way. Thus, we applied this idea in two algorithms, named unsupervised feature selection by particle swarm optimization and unsupervised feature selection by biased random-key genetic algorithm II. Finally, we simulated different datasets to study the effects of relevant and irrelevant attributes, and of noisy and missing data on the performance of the algorithms. After the Wilcoxon rank-sum test, we can state that the proposed algorithms outperform all other methods in different datasets. It was also observed that the construction of the initial population in a partially deterministic way contributed to the better performance. It should be noted that some methods are more sensitive to noisy and missing data than others, as well as to relevant and irrelevant attributes.

关键词： Unsupervised feature selection biased random-key genetic algorithm Particle swarm optimization Clustering Data mining Simulated datasets

来源：评论

学校读者我要写书评

暂无评论

Heuristics for a Hub Location-Routing Problem

引用

NETWORKS 2016年第1期68卷 54-90页

作者： Lopes, Mauro Cardoso de Andrade, Carlos Eduardo de Queiroz, Thiago Alves Resende, Mauricio G. C. Miyazawa, Flavio Keidi Univ Estadual Campinas Inst Comp Ave Albert Einstein 1251 BR-13083852 Campinas SP Brazil AT & T Labs Res Adv Technol Dept 200 South Laurel Ave Middletown NJ 07748 USA Univ Fed Goias Inst Math & Technol Ave Dr Lamartine Pinto de Avelar 1120 BR-75704020 Catalao Go Brazil Amazon Com Inc Math Optimizat & Planning Grp 333 Boren Ave North Seattle WA 98109 USA Univ Estadual Campinas Inst Comp Ave Albert Einstein 1251 BR-13083852 Campinas SP Brazil

We investigate a variant of the many-to-many hub location-routing problem which consists in partitioning the set of nodes of a graph into routes containing exactly one hub each, and determining an extra route interconnecting all hubs. A variable neighborhood descent with neighborhood structures based on remove/add, swap and exchange moves nested with routing and location operations is used as a local search procedure in a multistart algorithm. We also consider a sequential version of this local search in the multistart. In addition, a biased random-key genetic algorithm working with a local search routine, which also considers routing and location operations, is applied to the problem. To compare the heuristic solutions, we develop an integer programming formulation which is solved with a branch-andcut algorithm. Capacity and path elimination constraints are added in a cutting plane fashion. The separation algorithms are based on the computation of min-cut trees and on the connected components of a support graph. Computational experiments were conducted on several benchmark instances of routing problems and show that the heuristics are effective on medium to large-sized instances, while the branch-and-cut algorithm solves small to medium sized problems to optimality. These algorithms were also compared with a commercial hybrid solver showing that the heuristics are quite competitive. (C) 2016 Wiley Periodicals, Inc.

关键词： Hub location-routing problem heuristics variable neighborhood descent biased random-key genetic algorithm integer formulation

来源：评论

学校读者我要写书评

暂无评论

Evolutionary algorithm for Optimization Regarding the Planning of Topological Facilities in Layout of a Shipyard

引用

IEEE LATIN AMERICA TRANSACTIONS 2019年第9期17卷 1491-1500页

作者： Azzolini, W. Azzolini, F. Univ Sao Paulo Escola Engn Sao Carlos Sao Carlos SP Brazil UNESP Curso Ciencias Comp Bauru SP Brazil

The purpose of this study is to contribute the approach to the problem of optimization regarding the planning of topological facilities in layout of a shipyard, with the objective of finding a robust solution to the problem by improving the solution space search through refining the genetic operators. For this, the computational results of the evolutionary algorithm proposed by Choi with changes made by the authors, being: 1) the use of the Partially-Matched Crossover (PMX) genetic operator;2) the use of a recursive expression in the topological optimization step in addition to implementing the biased random-key genetic algorithm (BRKGA) for the purpose of comparing the results. As a plan of the computational experiments two groups of experiments were performed: 1) with the parameters and variables of the work of Choi, in order to validate the efficiency and effectiveness of the AE proposed in this work and;2) with the parameters and variables of the work of Choi with Department 03 fixed in the position of the best solution found in the 1st group of experiments (position 11 of the topological Grid). Each group contains 50 experiments with 100 iterations and variation of the number of individuals from 100 to 80,000 individuals. As a result, a better solution characterized by the reduction of material handling costs, of 11,816 presented by Choi, for 11,489 monetary units of cost, found from the changes made by the authors of the original proposal of the evolutionary algorithm and the use of BRKGA.

关键词： Evolutionary algorithm Facilities Planning Minimization of Materials Movement Cost Shipyard Topological Optimization biased random-key genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

A Hybrid BRKGA for Joint Scheduling Production, Transport, and Storage/Retrieval in Flexible Job Shops

A Hybrid BRKGA for Joint Scheduling Production, Transport, a...

引用

genetic and Evolutionary Computation Conference (GECCO)

作者： Homayouni, S. Mahdi Fontes, Dalila B. M. M. Fontes, Fernando A. C. C. CESE INESC TEC Campus FEUPRua Dr Roberto P-4200465 Porto Portugal Univ Porto Fac Econ INESC TEC LIAAD Porto Portugal Univ Porto Fac Engn SYSTEC ARISE Porto Portugal

ISBN: (纸本)9798400701207

This paper addresses the joint scheduling of production operations, transport tasks, and storage/retrieval activities in flexible job shop systems where the production operations and transport tasks can be done by one of the several resources available. Jobs need to be retrieved from storage and delivered to a load/unload area, from there, they are transported to and between the machines where their operations are processed on. Once all operations of a job are processed, the job is taken back to the load/unload area and then returned to the storage cell. Therefore, the problem under study requires, concurrently, solving job routing, machine scheduling, transport allocation, vehicle scheduling, and shuttle schedule. To this end, we propose a hybrid biased random-key genetic algorithm (BRKGA) in which the mutation operator resorts to six local search heuristics. The computational experiments conducted on a set of benchmark instances show the effectiveness of the proposed mutation operator.

关键词： flexible job shop scheduling problem joint scheduling biased random-key genetic algorithm joint scheduling storage/ retrieval

来源：评论

学校读者我要写书评

暂无评论

A BRKGA with Implicit Path-Relinking for the Vehicle Routing Problem with Occasional Drivers and Time Windows 1

引用

14th International Conference on Metaheuristics (MIC)

作者： Festa, Paola Guerriero, Francesca Resende, Mauricio G. C. Scalzo, Edoardo Univ Napoli Federico II Dept Math & Applicat I-80138 Naples Italy Univ Calabria Dept Mech Energy & Management Engn I-87036 Arcavacata Di Rende CS Italy Amazon Com Inc 333 108th Ave NE Bellevue WA 98004 USA Univ Washington 3900 E Stevens Way NE Seattle WA 98195 USA

ISBN: (数字)9783031265044

ISBN: (纸本)9783031265037;9783031265044

This paper describes a biased random-key genetic algorithm (BRKGA) with implicit path-relinking for the Vehicle Routing Problem with Occasional Drivers (VRPOD). After a review of the relevant literature, the paper describes a proposed decoder and how BRKGA parameters are set. Experimental results show the efficacy of the proposed approach.

关键词： Occasional drivers Vehicle routing problem biased random-key genetic algorithm Restart strategy

来源：评论

学校读者我要写书评

暂无评论

genetic algorithm for the Knapsack Problem with Irregular Shaped Items 44

Genetic Algorithm for the Knapsack Problem with Irregular Sh...

引用

44th Latin American Computing Conference on Informatics (CLEI)

作者： Queiroz, Layane R. S. Andretta, Marina Mundim, Leandro R. Univ Sao Paulo Inst Ciencias Matemat & Comp Sao Carlos SP Brazil Optimized Decis Making Sao Carlos SP Brazil

ISBN: (纸本)9781728104379

The two-dimensional knapsack problem with irregularly shaped items is solved in this work. It is utilized the concept of inner-fit raster and no-fit raster to verify packing feasibility, which stands for non-overlapping between items that are entirely contained inside the bin. The problem solution is obtained with a biased random-key genetic algorithm in which each chromosome contains information related to the order and rotation where each item should be packed into the bin. The chromosome also contains information about which heuristic has to be used to pack items and the probability of an offspring inheriting information from an elite parent. It is adopted three heuristics for positioning items, which are: bottom-left, left-bottom, and horizontal zig-zag. The experiments over literature instances showed that the developed genetic algorithm is very effective since it could obtain an optimal solution for 53.4% of the instances and improved the bin's occupancy ratio in about 2.1% when observing all the instances.

关键词： biased random-key genetic algorithm Two-dimensional Knapsack Problem Irregularly shaped items Nesting problems

来源：评论

学校读者我要写书评

暂无评论

The P-Next Center Problem with Capacity and Coverage Radius Constraints: Model and Heuristics 14th

The P-Next Center Problem with Capacity and Coverage Radius ...

引用

14th International Conference on Metaheuristics (MIC)

作者： Londe, Mariana A. Pessoa, Luciana S. Andrade, Carlos E. Pontificia Univ Catolica Rio de Janeiro Dept Ind Engn Rua Marques Sae Vicente 225 BR-22453900 Rio de Janeiro RJ Brazil AT&T Labs Res 200 South Laurel Ave Middletown NJ 07748 USA

ISBN: (纸本)9783031265037;9783031265044

This paper introduces a novel problem of facility location, called the p-next center problem with capacity and coverage radius constraints. We formulate a mixed integer programming model for this problem, and compare the results found by CPLEX with three biased random-key genetic algorithms variants. We also propose several instances for this problem, based on existing ones for the p-next center problem. Additionally, we analyze the effect of the radius and demand on instance difficulty. We also observe the performance gains with a relaxed capacity and demand constraint, i.e., permitting demand to be unmet by the model. Results point that the BRKGA variants had significantly better performance than CPLEX, and similar performances among themselves. Of those, BRKGA-FI was shown to have slightly better results than the other variants.

关键词： Facility location biased random-key genetic algorithm Metaheuristics

来源：评论

学校读者我要写书评

暂无评论

Superior genetic algorithms for the Target Set Selection Problem Based on Power-Law Parameter Choices and Simple Greedy Heuristics

Superior Genetic Algorithms for the Target Set Selection Pro...

引用

genetic and Evolutionary Computation Conference (GECCO)

作者： Doerr, Benjamin Krejca, Martin S. Nguyen Vu Ecole Polytech CNRS Inst Polytech Paris Lab Informat LIX Palaiseau France Ecole Polytech Inst Polytech Paris Palaiseau France

ISBN: (纸本)9798400704949

The target set selection problem (TSS) asks for a set of vertices such that an influence spreading process started in these vertices reaches the whole graph. The current state of the art for this NP-hard problem are three recently proposed randomized search heuristics, namely a biased random-key genetic algorithm (BRKGA) obtained from extensive parameter tuning, a max-min ant system (MMAS), and a MMAS using Q-learning with a graph convolutional network. We show that the BRKGA with two simple modifications and without the costly parameter tuning obtains significantly better results. Our first modification is to simply choose all parameters of the BRKGA in each iteration randomly from a power-law distribution. The resulting parameterless BRKGA is already competitive with the tuned BRKGA, as our experiments on the previously used benchmarks show. We then add a natural greedy heuristic, namely to repeatedly discard small-degree vertices that are not necessary for reaching the whole graph. The resulting algorithm consistently outperforms all of the state-of-the-art algorithms. Besides providing a superior algorithm for the TSS problem, this work shows that randomized parameter choices and elementary greedy heuristics can give better results than complex algorithms and costly parameter tuning.

关键词： Target set selection combinatorial optimization biased random-key genetic algorithm parameter tuning

来源：评论

学校读者我要写书评

暂无评论

Evolutionary algorithms for Overlapping Correlation Clustering 14

Evolutionary Algorithms for Overlapping Correlation Clusteri...

引用

16th genetic and Evolutionary Computation Conference (GECCO)

作者： Andrade, Carlos E. Resende, Mauricio G. C. Karloff, Howard J. Miyazawa, Flavio K. Univ Estadual Campinas BR-1251 Campinas SP Brazil AT&T Labs Res Middletown NJ 07748 USA Yahoo Labs New York NY 10018 USA

ISBN: (纸本)9781450326629

In Overlapping Correlation Clustering (OCC), a number of objects are assigned to clusters. Two objects in the same cluster have correlated characteristics. As opposed to traditional clustering where objects are assigned to a single cluster, in OCC objects may be assigned to one or more clusters. In this paper, we present biased random-key genetic algorithms for OCC. We present computational experiments such results outperformed the state of art methods for OCC.

关键词： Data mining Correlation Clustering biased random-key genetic algorithm

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：