Journal ArticleDOI
A survey for the quadratic assignment problem
Eliane Maria Loiola,Nair Maria Maia de Abreu,Paulo Oswaldo Boaventura-Netto,Peter M. Hahn,Tania Maia Querido +4 more
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This paper presents some of the most important QAP formulations and classify them according to their mathematical sources and gives a detailed discussion of the progress made in both exact and heuristic solution methods, including those formulated according to metaheuristic strategies.About:
This article is published in European Journal of Operational Research.The article was published on 2007-01-16. It has received 648 citations till now. The article focuses on the topics: Quadratic assignment problem & Combinatorial optimization.read more
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Computational Optimal Transport
Gabriel Peyré,Marco Cuturi +1 more
TL;DR: This short book reviews OT with a bias toward numerical methods and their applications in data sciences, and sheds lights on the theoretical properties of OT that make it particularly useful for some of these applications.
Proceedings ArticleDOI
Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement
TL;DR: This paper designs a two-tier approximate algorithm that efficiently solves the VM placement problem for very large problem sizes and shows a significant performance improvement compared to existing general methods that do not take advantage of traffic patterns and data center network characteristics.
Proceedings ArticleDOI
SuperGlue: Learning Feature Matching With Graph Neural Networks
TL;DR: SuperGlue is introduced, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points and introduces a flexible context aggregation mechanism based on attention, enabling SuperGlue to reason about the underlying 3D scene and feature assignments jointly.
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SuperGlue: Learning Feature Matching with Graph Neural Networks
TL;DR: SuperGlue as discussed by the authors matches two sets of local features by jointly finding correspondences and rejecting non-matchable points by solving a differentiable optimal transport problem, whose costs are predicted by a graph neural network.
Journal ArticleDOI
Chemical-Reaction-Inspired Metaheuristic for Optimization
Albert Y. S. Lam,Victor O. K. Li +1 more
TL;DR: This work proposes a new metaheuristic, called chemical reaction optimization (CRO), which mimics the interactions of molecules in a chemical reaction to reach a low energy stable state and can outperform all other metaheuristics when matched to the right problem type.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Journal ArticleDOI
Ant system: optimization by a colony of cooperating agents
TL;DR: It is shown how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling, and the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
Journal ArticleDOI
Tabu Search—Part II
TL;DR: The elements of staged search and structured move sets are characterized, which bear on the issue of finiteness, and new dynamic strategies for managing tabu lists are introduced, allowing fuller exploitation of underlying evaluation functions.