1,488 research outputs found
Is the physical vacuum a preferred frame ?
It is generally assumed that the physical vacuum of particle physics should
be characterized by an energy momentum tensor in such a way to preserve exact
Lorentz invariance. On the other hand, if the ground state were characterized
by its energy-momentum vector, with zero spatial momentum and a non-zero
energy, the vacuum would represent a preferred frame. Since both theoretical
approaches have their own good motivations, we propose an experimental test to
decide between the two scenarios.Comment: 12 pages, no figure
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Combinatorial optimization and metaheuristics
Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (âefficientâ) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find âquicklyâ (reasonable run-times), with âhighâ probability, provable âgoodâ solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods
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Metaheuristic approaches for the quartet method of hierarchical clustering
Given a set of objects and their pairwise distances, we wish to determine a visual representation of the data. We use the quartet paradigm to compute a hierarchy of clusters of the objects. The method is based on an NP-hard graph optimization problem called the Minimum Quartet Tree Cost problem. This paper presents and compares several metaheuristic approaches to approximate the optimal hierarchy. The performance of the algorithms is tested through extensive computational experiments and it is shown that the Reduced Variable Neighbourhood Search metaheuristic is the most effective approach to the problem, obtaining high quality solutions in short computational running times
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Variable neighbourhood search for the minimum labelling Steiner tree problem
We present a study on heuristic solution approaches to the minimum labelling Steiner tree problem, an NP-hard graph problem related to the minimum labelling spanning tree problem. Given an undirected labelled connected graph, the aim is to find a spanning tree covering a given subset of nodes of the graph, whose edges have the smallest number of distinct labels. Such a model may be used to represent many real world problems in telecommunications and multimodal transportation networks. Several metaheuristics are proposed and evaluated. The approaches are compared to the widely adopted Pilot Method and it is shown that the Variable Neighbourhood Search that we propose is the most effective metaheuristic for the problem, obtaining high quality solutions in short computational running time
Constructive Heuristics for the Minimum Labelling Spanning Tree Problem: a preliminary comparison
This report studies constructive heuristics for the minimum labelling spanning tree
(MLST) problem. The purpose is to find a spanning tree that uses edges that are as similar as
possible. Given an undirected labeled connected graph (i.e., with a label or color for each edge),
the minimum labeling spanning tree problem seeks a spanning tree whose edges have the smallest
possible number of distinct labels. The model can represent many real-world problems in
telecommunication networks, electric networks, and multimodal transportation networks, among
others, and the problem has been shown to be NP-complete even for complete graphs. A primary
heuristic, named the maximum vertex covering algorithm has been proposed. Several versions of
this constructive heuristic have been proposed to improve its efficiency. Here we describe the
problem, review the literature and compare some variants of this algorithm
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Heuristics based on greedy randomized adaptive search and variable neighbourhood search for the minimum labelling spanning tree problem
This paper studies heuristics for the minimum labelling spanning tree (MLST) problem. The purpose is to find a spanning tree using edges that are as similar as possible. Given an undirected labelled connected graph, the minimum labelling spanning tree problem seeks a spanning tree whose edges have the smallest number of distinct labels. This problem has been shown to be NP-complete. A Greedy Randomized Adaptive Search Procedure (GRASP) and different versions of Variable Neighbourhood Search (VNS) are proposed. They are compared with other algorithms recommended in the literature: the Modified Genetic Algorithm and the Pilot Method. Nonparametric statistical tests show that the heuristics based on GRASP and VNS outperform the other algorithms tested. Furthermore, a comparison with the results provided by an exact approach shows that we may quickly obtain optimal or near-optimal solutions with the proposed heuristics
Rapporto sulle nuove misure di CO2 nella galleria drenante Pavone di Vena (06 Settembre 2006)
Lâintervento Ăš stato effettuato su segnalazione fatta il giorno 30 Agosto 2006 dal
Geometra Alletto a S. Giammanco. Secondo quanto riferito dal Geom. Alletto, a partire da
quella mattina si stava verificando un forte accumulo di gas allâinterno della galleria
drenante Pavone ubicata in Contrada Rocca Campana di Vena, tanto da impedirne
lâaccesso al personale operaio ivi presente quotidianamente. Tale fenomeno risultava
simile a quello segnalato nel Novembre 2005, a seguito del quale si era effettuato un
precedente intervento (vedasi rapporto interno UFVG2005/115)
Discrete Particle Swarm Optimization for the minimum labelling Steiner tree problem
Particle Swarm Optimization is an evolutionary method inspired by the
social behaviour of individuals inside swarms in nature. Solutions of the problem are
modelled as members of the swarm which fly in the solution space. The evolution is
obtained from the continuous movement of the particles that constitute the swarm
submitted to the effect of the inertia and the attraction of the members who lead the
swarm. This work focuses on a recent Discrete Particle Swarm Optimization for combinatorial optimization, called Jumping Particle Swarm Optimization. Its effectiveness is
illustrated on the minimum labelling Steiner tree problem: given an undirected labelled
connected graph, the aim is to find a spanning tree covering a given subset of nodes,
whose edges have the smallest number of distinct labels
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