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2021 Genetic and Evolutionary Computation Conference, GECCO 2021

作者： Lopes, Claudio L. V. Martins, Flávio V. C. Wanner, Elizabeth F. Deb, Kalyanmoy Postgraduate Program in Mathematical and Computational Modeling CEFET-MG BH Mg Brazil Computer Department CEFET-MG BH Mg 30510-000 Brazil Computer Department CEFET-MG BH Mg Brazil

ISBN: (纸本)9781450383516

Dominance move (DoM) is a quality indicator that compares two solution sets in a Pareto-optimal sense. The main issue related to DoM is its computational expense. A recent paper proposed a mixed-integer programming (MIP) approach for computing DoM that exhibited a computational complexity that is linear to the number of objectives and polynomial to the number of solutions. Even with this property, considering practical situations, the MIP-DoM calculation on some problems may take many hours. This paper presents an approximation method to deal with the problem using a cluster-based and divide-and-conquer strategy. Some experiments are tested, showing that the cluster based-algorithm is computationally much faster and makes a small percentage error from the original DoM value. © 2021 Owner/Author.

关键词： Multiobjective optimization

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Google COVID-19 community mobility reports: Insights from multi-criteria decision making

arXiv

引用

arXiv 2020年

作者： da Silva, Gabriela Cavalcante Oliveira, Sabrina Wanner, Elizabeth F. Bezerra, Leonardo C.T. Brazil Postgraduate Program in Mathematical and Computational Modeling CEFET-MG Brazil Computer Engineering Dept. CEFET-MG Brazil Computer Science Group Aston University United Kingdom

Social distancing (SD) has been critical in the fight against the novel coronavirus disease (COVID-19). To aid SD monitoring, many technology companies have made available mobility data, the most prominent example being the community mobility reports (CMR) provided by Google. Given the wide range of research fields that have been drawing insights from CMR data, there has been a rising concern for methodological discussion on how to use them. Indeed, Google recently released their own guidelines, concerning the nature of the place categories and the need for calibrating regional values. In this work, we discuss how measures developed in the field of multi-criteria decision making (MCDM) might benefit researchers analyzing this data. Concretely, we discuss how Pareto dominance and performance measures adopted in MCDM enable the mobility evaluation for (i) multiple categories for a given time period and (ii) multiple categories over multiple time periods. We empirically demonstrate these approaches conducting both a region- and country-level analysis, comparing some of the most relevant outbreak examples from different continents. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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Urban Transportation Planning, Sustainable Cities, Big Data, Intelligent Transport Systems

SSRN

引用

SSRN 2024年

作者： Vieira, Túlio Moita, Gray Ribeiro, Renato Meireles, Magali Postgraduate Program in Mathematical and Computational Modeling CEFET-MG Av. Amazonas 7675 Minas Gerais Belo Horizonte30510-000 Brazil Transport Engineering Department CEFET-MG Av. Amazonas 7675 Minas Gerais Belo Horizonte30510-000 Brazil Institute of Mathematical Sciences and Informatics PUC-Minas Walter Ianni 255 Minas Gerais Belo Horizonte31980-110 Brazil

This paper presents a comprehensive study on the critical role of efficient transportation systems in urban settings, particularly in developing countries. It underscores the socio-economic and environmental impacts of inadequate urban planning and highlights the disproportionate effects on the most vulnerable populations. The study proposes a novel, low-cost framework that leverages advancements in artificial intelligence, information systems, and hardware to improve the efficiency and accessibility of public transportation systems. The framework enables the construction of automatic origin-destination matrices and continuous monitoring of key transportation indicators, facilitating more accurate and timely decision-making. The paper also presents a comparative analysis of the proposed methodology with existing ones in the literature, validating its effectiveness with field-collected data. The study concludes by emphasizing thepotential of technology in addressing urban transportation challenges, contributing to the broader goal of creating sustainable and inclusive cities. This work serves as a valuable tool for urban planners in resource-constrained settings, bridging the gap between advanced technology and urban planning. © 2024, The Authors. All rights reserved.

关键词： Urban transportation

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Analyzing dominance move (MIP-DoM) indicator for multi- And many-objective optimization

arXiv

引用

arXiv 2020年

作者： do Val Lopes, Claudio Lucio Martins, Flávio Vinícius Cruzeiro Wanner, Elizabeth Fialho Deb, Kalyanmoy Postgraduate Program in Mathematical and Computational Modeling CEFET-MG Brazil Computer Department Centro Federal de Educação Tecnológica de Minas Gerais 30510-000 Brazil Computer Department Centro Federal de Educação Tecnológica de Minas Gerais 30510-000 Brazil Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States

—Dominance move (DoM) is a binary quality indicator that can be used in multi-objective and many-objective optimization to compare two solution sets obtained from different algorithms. The DoM indicator can differentiate the sets for certain important features, such as convergence, spread, uniformity, and cardinality. DoM does not use any reference, and it has an intuitive and physical meaning, similar to the -indicator, and calculates the minimum total move of members of one set so that all elements in another set are to be dominated or identical to at least one member of the first set. Despite the aforementioned properties, DoM is hard to calculate, particularly in higher dimensions. There is an efficient and exact method to calculate it in a bi-objective case only. This work proposes a novel approach to calculate DoM using a mixed integer programming (MIP) approach, which can handle sets with three or more objectives and is shown to overcome the -indicator’s information loss. Experiments, in the bi-objective space, are done to verify the model’s correctness. Furthermore, other experiments, using 3, 5, 10, 15, 20, 25 and 30-objective problems are performed to show how the model behaves in higher-dimensional cases. Algorithms, such as IBEA, MOEA/D, NSGA-III, NSGA-II, and SPEA2 are used to generate the solution sets (however any other algorithms can also be used with the proposed MIP-DoM indicator). Further extensions are discussed to handle certain idiosyncrasies with some solution sets and also to improve the quality indicator and its use for other situations. Copyright © 2020, The Authors. All rights reserved.

关键词： Multiobjective optimization

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