检索结果-内蒙古大学图书馆

Application of a hybrid evolutionary algorithm to resource-constrained parallel machine scheduling with setup times

COMPUTERS & OPERATIONS RESEARCH 2022年 139卷

作者： Soares, Leonardo C. R. Carvalho, Marco A. M. Inst Fed Sudeste Minas Gerais Campus Manhuacu BR-36909300 Manhuacu MG Brazil Univ Fed Ouro Preto Campus Morro Cruzeiro BR-35400000 Ouro Preto MG Brazil

We address the problem of resource-constrained parallel machine scheduling with setup times in the practical context of microelectronic components manufacturing. This NP-hard problem is addressed using a biased random-key genetic algorithm hybridized with tailored local search procedures organized using variable neighborhood descent. The only benchmark available in the literature is utilized, and the optimal results are presented for all instances. Two new sets with 270 challenging instances are proposed to assess the quality of the solutions reported by the proposed method. A series of experiments are conducted to generate lower and upper bounds, including four models, two list processing heuristics from the literature and an implementation of a general variable neighborhood search metaheuristic. The bounds are used as reference values. The average percentage distances from the lower and upper bounds were 22.44% and -7.62%, respectively.

关键词： Combinatorial problems Metaheuristic biased random-key genetic algorithm Resource-constrained parallel machine scheduling with setup times

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

A BRKGA-based matheuristic for the maximum quasi-clique problem with an exact local search strategy

引用

RAIRO-OPERATIONS RESEARCH 2021年第1期55卷 S741-S763页

作者： Pinto, Bruno Q. Ribeiro, Celso C. Riveaux, Jose A. Rosseti, Isabel Inst Fed Educ Ciencia & Tecnol Triangulo Mineiro BR-38411104 Uberlandia MG Brazil Univ Fed Fluminense Inst Comp BR-24210346 Niteroi RJ Brazil

Given a graph G = (V, E) and a threshold gamma is an element of (0, 1], 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 has a number of applications in data mining, e.g., in social networks or phone call graphs. We propose a matheuristic for solving the maximum cardinality quasi-clique problem, based on the hybridization of a biased random-key genetic algorithm (BRKGA) with an exact local search strategy. The newly proposed approach is compared with a pure biased random-key genetic algorithm, which was the best heuristic in the literature at the time of writing. Computational results show that the hybrid BRKGA outperforms the pure BRKGA.

关键词： Maximum quasi-clique problem maximum clique problem biased random-key genetic algorithm metaheuristics matheuristics

来源：评论

学校读者我要写书评

暂无评论

An evolutionary approach for the p-next center problem

引用

EXPERT SYSTEMS WITH APPLICATIONS 2021年 175卷

作者： Londe, Mariana A. Andrade, Carlos E. Pessoa, Luciana S. Pontificia Univ Catolica Rio de Janeiro Dept Ind Engn Rua Marques Sao Vicente 225 BR-22453900 Rio de Janeiro RJ Brazil AT&T Labs Res 5312 SW 6th Pl Ocala FL 34474 USA

The p-next center problem is an extension of the classical p-center problem, in which a backup center must be assigned to welcome users from a suddenly unavailable center. Usually, users tend to seek help in the closest facility they can find. However, during a significant event or crisis, one only realizes that the closest facility has been disrupted upon his/her arrival. Therefore, the user seeks help in the next closest center from the one that has failed to provide service. Therefore, the objective of the p-next center problem is to minimize the path of any user, which is made by the distance from this origin to its closest installed facility, plus the distance from this facility to its backup. We propose an evolutionary approach for the p-next center problem and an extension for the current benchmark instances. The proposed methods are built on the Multi-Parent biased random-key genetic algorithm with Implicit Path-Relinking. Computational experiments carried out on 416 test instances show, experimentally, the outstanding performance of the developed algorithms and their flexibility to reach a good quality-speed trade-off.

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

Hybrid algorithms for placement of virtual machines across geo-separated data centers

引用

JOURNAL OF COMBINATORIAL OPTIMIZATION 2019年第3期38卷 748-793页

作者： Stefanello, Fernando Aggarwal, Vaneet Buriol, Luciana S. Resende, Mauricio G. C. Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil Purdue Univ Sch Ind Engn Sch Elect & Comp Engn W Lafayette IN 47907 USA Amazon Com Seattle WA 98109 USA Univ Washington Ind & Syst Engn Seattle WA 98105 USA

Cloud computing has emerged as a new paradigm for hosting and supplying services over the Internet. This technology has brought many benefits, such as eliminating the need for maintaining expensive computing hardware. 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 (VMPlacement), 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). In this paper, we present a greedy randomized adaptive search procedure and a biased random-key genetic algorithm, both hybridized with a path-relinking strategy and a local search based on variable neighborhood descent for solving this problem. The hybrid heuristics are also tested on instances of the GQAP. We show that both algorithms are effective in quickly solving small and large instances of VMPlacement problem, especially when the path-relinking is used. For GQAP, the results outperform the previous state-of-the-art algorithms.

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

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

Robust meter placement for state estimation considering Distribution Network Reconfiguration for annual energy loss reduction

引用

ELECTRIC POWER SYSTEMS RESEARCH 2020年 182卷

作者： Raposo, Antonio A. M. Rodrigues, Anselmo B. da Silva, Maria da Guia Univ Fed Maranhao Elect Engn Dept BR-65080805 Sao Luis MA Brazil

This paper proposes a methodology to allocate meters in distribution networks oriented to state estimation. The proposed approach considers that the meter placement is carried out simultaneously with the Distribution Network Reconfiguration (DNR) for annual energy loss reduction. This formulation of the allocation problem prevents the State Estimator Accuracy (SEA) from being degraded when the topology is changed and that an excessive number of meters is installed to obtain acceptable accuracy. These objectives were achieved using a multi-objective formulation that minimizes resistive losses, the risk of violating SEA and the number of meters installed in the network. The problem of meter placement formulated in this paper is solved through the Multi-Objective biased random-key genetic algorithm (MOBRKGA). The tests results demonstrate that MOBRKGA can generate high quality solutions with significant reduction in annual energy losses and metering system with good accuracy and low installation cost.

关键词： biased random-key genetic algorithm Multi-objective optimization Meter placement State estimation Network Reconfiguration Energy losses

来源：评论

学校读者我要写书评

暂无评论

Two effective methods for the irregular knapsack problem

引用

APPLIED SOFT COMPUTING 2020年 95卷

作者： de Souza Queiroz, Layane Rodrigues Andretta, Marina Univ Sao Paulo Inst Math & Comp Sci BR-13566590 Sao Carlos SP Brazil

Two methods are developed for a two-dimensional cutting problem with irregular shaped items. The concepts of inner-fit raster and no-fit raster are used to search for a feasible positioning of items on a rectangular container. The first method is a biased random key genetic algorithm, which is a population method, while the other is a Variable Neighborhood Search, which is a single trajectory method. In the proposed methods, a solution is represented by a vector of items, and the positioning of items is achieved with three rules inspired by the bottom-left strategy. When positioning items, feasible positions can be skipped as a strategy to diversify the search and escape from local optima solutions. Numerical experiments performed on literature instances show that the methods are better than the current state-of-the-art method since they obtained equal or better solutions for all the instances. On average, the occupied area increased 6.44%, and the known optimal solution was obtained for 60% of the instances. The population-based method performed better overall, obtaining solutions with better-occupied areas. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Two-dimensional Knapsack problem Irregular shaped items biased random-key genetic algorithm Variable neighborhood search Bottom-left

来源：评论

学校读者我要写书评

暂无评论

Optimization of the Numeric and Categorical Attribute Weights in KAMILA Mixed Data Clustering algorithm 1

引用

20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

作者： Martarelli, Nadia Junqueira Nagano, Marcelo Seido Univ Sao Paulo Lab Appl Operat Res BR-13566590 Sao Paulo Brazil

ISBN: (数字)9783030336073

ISBN: (纸本)9783030336073;9783030336066

The mixed data clustering algorithms have been timidly emerging since the end of the last century. One of the last algorithms proposed for this data-type has been KAMILA (KAy-means for MIxed LArge data) algorithm. While the KAMILA has outperformed the previous mixed data algorithms results, it has some gaps. Among them is the definition of numerical and categorical variable weights, which is a user-defined parameter or, by default, equal to one for all features. Hence, we propose an optimization algorithm called biased random-key genetic algorithm for Features Weighting (BRKGAFW) to accomplish the weighting of the numerical and categorical variables in the KAMILA algorithm. The experiment relied on six real-world mixed data sets and two baseline algorithms to perform the comparison, which are the KAMILA with default weight definition, and the KAMILA with weight definition done by the traditional genetic algorithm. The results have revealed the proposed algorithm overperformed the baseline algorithms results in all data sets.

关键词： Attributes weighting Mixed data clustering biased random-key genetic algorithm KAMILA 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 OSPF and DEFT routing to minimize network congestion

引用

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2011年第3期18卷 401-423页

作者： Reis, Roger Ritt, Marcus Buriol, Luciana S. Resende, Mauricio G. C. Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil AT&T Labs Res Algorithms & Optimizat Res Dept Florham Pk NJ 07932 USA

Interior gateway routing protocols like Open Shortest Path First (OSPF) and Distributed Exponentially Weighted Flow Splitting (DEFT) send flow through forward links toward the destination node. OSPF routes only on shortest-weight paths, whereas DEFT sends flow on all forward links, but with an exponential penalty on longer paths. Finding suitable weights for these protocols is known as the weight setting problem (WSP). In this paper, we present a biased random-key genetic algorithm for WSP using both protocols. The algorithm uses dynamic flow and dynamic shortest path computations. We report computational experiments that show that DEFT achieves less network congestion when compared with OSPF, while, however, yielding larger delays.

关键词： networks networking telecommunication networks routing genetic algorithm biased random-key genetic algorithm local search metaheuristics

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：