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

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

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Reconfiguração de sistemas de distribuição através do algoritmo genético de chaves aleatórias viciadas

Reconfiguração de sistemas de distribuição através do a...

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作者： Vargas Peralta, Rommel Gregorio Universidade Estadual Paulista

Nesta dissertação é proposta a aplicação do algoritmo genético de chaves aleatórias viciadas para a solução do problema de reconfiguração de sistemas de distribui... 详细信息

Nesta dissertação é proposta a aplicação do algoritmo genético de chaves aleatórias viciadas para a solução do problema de reconfiguração de sistemas de distribuição. Esse problema de otimização consiste em encontrar a configuração radial que apresenta perdas mínimas, satisfazendo as restrições topológicas e as restrições operacionais, sendo modelado como um problema de Programação Não Linear Inteira Mista. O método proposto utiliza o algoritmo de Prim na geração de configurações radiais e emprega um algoritmo de fluxo de carga de varredura para avaliar cada proposta de solução. O algoritmo genético de chaves aleatórias viciadas foi desenvolvido na linguagem de programação FORTRAN e foi testado em quatro sistemas de distribuição da literatura especializada (14 barras, 33 barras, 84 barras e 136 barras). Os resultados obtidos da aplicação do algoritmo permitem avaliar o seu desempenho e eficiência em comparação com a melhor solução encontrada na literatura *** application of the biased random-key genetic algorithm for the reconfiguration of distribution systems is proposed in this Dissertation. The problem of reconfiguration in distribution systems consists of finding the radial configuration that presents the minimum losses, satisfying topological and operating constraints and is commonly modeled as a mixed-integer nonlinear programming problem. The proposed method uses the Prims algorithm to generate radial configurations that are evaluated through a backward/forward sweep power flow method. The biased random-key genetic algorithm used was developed in the programming language FORTRAN and was tested in four systems (14-bus, 33-bus, 84-bus and 136-bus). The obtained results show the performance and efficiency of the proposed method in comparison to the best solution found in the specialized literature.

关键词： Reconfiguração de sistemas de distribuição Algoritmo genético das chaves aleatórias viciadas Algoritmo de Prim Fluxo de carga de varredura para sistemas radiais Distribution system reconfiguration biased random-key genetic algorithm Prim’s algorithm

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An evolutionary approach for the p-next center problem

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 175卷 114728-114728页

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

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

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RAIRO-OPERATIONS RESEARCH 2021年第1-Sup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

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Heuristic approaches for scheduling of a two-machine flowshop with outsourcing lead-time

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ENGINEERING OPTIMIZATION 2024年第12期56卷 2431-2449页

作者： Kim, Eun-Seok Lee, Ik Sun Queen Mary Univ London Sch Business & Management London England Dong A Univ Sch Business Busan South Korea

This article studies a scheduling problem of a two-machine flowshop , considering outsourcing lead-time, where outsourcing a job incurs outsourcing cost as well as outsourcing lead-time. Each job has an option of being outsourced, i.e. the first operation of the outsourced job is handled by an external subcontractor while the second operation must be processed in-house after its delivery. The objective is to find a schedule for minimizing the weighted sum of the outsourcing costs and the total completion time. For the problem, the subset of jobs to be outsourced and the processing order of jobs are interwoven, implying that decisions have to be made simultaneously. This study examines the properties of the optimal solution and proposes various heuristics based on iterative local search and the biased random-key genetic algorithm. Extensive computational experiments are carried out in various settings.

关键词： Scheduling outsourcing two-machine flowshop heuristic biased random-key genetic algorithm

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

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ELECTRIC POWER SYSTEMS RESEARCH 2020年第0期182卷 106233-000页

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

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Scheduling software updates for connected cars with limited availability

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APPLIED SOFT COMPUTING 2019年 82卷 105575-000页

作者： Andrade, Carlos E. Byers, Simon D. Gopalakrishnan, Vijay Halepovic, Emir Poole, David J. Tran, Lien K. Volinsky, Christopher T. AT&T Labs Res 200 South Laurel Ave Middletown NJ 07748 USA AT&T Labs Res 1 AT&T Way Bedminster NJ 07921 USA

The current and the new generation of Internet of Things (IoT) devices present several challenges, among them the software update of legacy and new devices using wireless connections. In this paper, we study a problem of scheduling massive Firmware-Over-The-Air updates for millions of connected cars. We model this problem as a new generic problem called Time- and Machine-Dependent Scheduling Problem (TMDSP) that resembles project scheduling problems with variable-intensity activities. In the TMDSP, jobs, machine utilization, and production rates vary over time. In each period, a given job can be executed by a subset of machines with different capacities and production rates. The objective is to deploy a feasible schedule with minimum total completion time. We explore solving the problem using several approaches among than biased random-key genetic algorithm (BRKGA), Iterated Local Search (ILS), Simulating Annealing (SA), two variations of Tabu Search (TB), and traditional genetic algorithms (TGA), in addition to a Mixed Integer Programming (MIP) model. We test the proposed approaches on a synthetic benchmark and very large real instances in IoT space. While using a commercial solver and the MIP model, we are able to solve only a few real instances (with, at most, 1,976 cars and 9,116 sectors);on the other hand, BRKGA presents significantly better results when compared to ILS, MIP solver and a simple multi-start heuristic. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Connected cars Internet of Things Scheduling Time-and machine-dependent scheduling biased random-key genetic algorithm

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Two effective methods for the irregular knapsack problem

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APPLIED SOFT COMPUTING 2020年 95卷 106485-106485页

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

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

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COMPUTERS & OPERATIONS RESEARCH 2022年 139卷 1页

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

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Dynamic Cargo Stability in Loading and Transportation of Containers

Dynamic Cargo Stability in Loading and Transportation of Con...

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IEEE International Conference on Automation Science and Engineering

作者： Evandro C. Bracht Thiago A. de Queiroz Rafael C. S. Schouery Flavio K. Miyazawa Institute of Computing Institute of Mathematics and Technology

ISBN: (纸本)9781509024100

We present an approach to deal with the problem of packing boxes in a container with fixed dimensions (the container loading problem) constrained that the packing must be dynamically stable, that is, no box can move due to forces such as gravity and horizontal forces caused by transportation of the container. To solve this problem, an approach based on the metaheuristic BRKGA (biased random-key genetic algorithm) considering the geometrical nature of the problem, in order to better combine two solutions, is considered. The approach uses a physical simulation package to determine if the given packing is, indeed, dynamically stable. The results show that this approach can later be used as a tool for automated processes involving loading and transportation of containers.

关键词： Cargo stability Container loading biased random-key genetic algorithm Physics engine

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