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Sergio Consoli

Publications and source records attributed to Sergio Consoli.

27 records · Page 2Linked to original sources
arXiv2018

Minimum Labelling bi-Connectivity

A labelled, undirected graph is a graph whose edges have assigned labels, from a specific set. Given a labelled, undirected graph, the well-known minimum labelling spanning tree problem is aimed at finding the spanning tree of the graph with the minimum set of labels. This combinatorial problem, which is NP-hard, can be also formulated as to give the minimum number of labels that provide single connectivity among all the vertices of the graph. Here we consider instead the problem of finding the minimum set of labels that provide bi-connectivity among all the vertices of the graph. A graph is bi-connected if there are at least two disjoint paths joining every pair of vertices. We consider both bi-connectivity concept: the edge bi-connectivity where these paths cannot have a common edge and the vertex bi-connectivity where the paths cannot have a common vertex. We describe our preliminary investigation on the problem and provide the details on the solution approaches for the problem under current development.

cs.DM↗
arXiv2016

An Innovative, Open, Interoperable Citizen Engagement Cloud Platform for Smart Government and Users' Interaction

This paper introduces an open, interoperable, and cloud-computing-based citizen engagement platform for the management of administrative processes of public administrations, which also increases the engagement of citizens. The citizen engagement platform is the outcome of a 3-year Italian national project called PRISMA (Interoperable cloud platforms for smart government). The aim of the project is to constitute a new model of digital ecosystem that can support and enable new methods of interaction among public administrations, citizens, companies, and other stakeholders surrounding cities. The platform has been defined by the media as a flexible (enable the addition of any kind of application or service) and open (enable access to open services) Italian "cloud" that allows public administrations to access to a vast knowledge base represented as linked open data to be reused by a stakeholder community with the aim of developing new applications ("Cloud Apps") tailored to the specific needs of citizens. The platform has been used by Catania and Syracuse municipalities, two of the main cities of southern Italy, located in the Sicilian region. The fully adoption of the platform is rapidly spreading around the whole region (local developers have already used available application programming interfaces (APIs) to create additional services for citizens and administrations) to such an extent that other provinces of Sicily and Italy in general expressed their interest for its usage. The platform is available online and, as mentioned above, is open source and provides APIs for full exploitation.

cs.CY↗
arXiv2015

An intelligent extension of Variable Neighbourhood Search for labelling graph problems

In this paper we describe an extension of the Variable Neighbourhood Search (VNS) which integrates the basic VNS with other complementary approaches from machine learning, statistics and experimental algorithmic, in order to produce high-quality performance and to completely automate the resulting optimization strategy. The resulting intelligent VNS has been successfully applied to a couple of optimization problems where the solution space consists of the subsets of a finite reference set. These problems are the labelled spanning tree and forest problems that are formulated on an undirected labelled graph; a graph where each edge has a label in a finite set of labels L. The problems consist on selecting the subset of labels such that the subgraph generated by these labels has an optimal spanning tree or forest, respectively. These problems have several applications in the real-world, where one aims to ensure connectivity by means of homogeneous connections.

cs.AI↗
arXiv2015

Towards an intelligent VNS heuristic for the k-labelled spanning forest problem

In a currently ongoing project, we investigate a new possibility for solving the k-labelled spanning forest (kLSF) problem by an intelligent Variable Neighbourhood Search (Int-VNS) metaheuristic. In the kLSF problem we are given an undirected input graph G and an integer positive value k, and the aim is to find a spanning forest of G having the minimum number of connected components and the upper bound k on the number of labels to use. The problem is related to the minimum labelling spanning tree (MLST) problem, whose goal is to get the spanning tree of the input graph with the minimum number of labels, and has several applications in the real world, where one aims to ensure connectivity by means of homogeneous connections. The Int-VNS metaheuristic that we propose for the kLSF problem is derived from the promising intelligent VNS strategy recently proposed for the MLST problem, and integrates the basic VNS for the kLSF problem with other complementary approaches from machine learning, statistics and experimental algorithmics, in order to produce high-quality performance and to completely automate the resulting strategy.

cs.OH↗
arXiv2015

BVNS para el problema del bosque generador k-etiquetado

In this paper we propose an efficient solution for the problem of generating k-labeling forest VNS. This problem is an extension of the Minimum Spanning Tree Problem Labelling problem with important applications in telecommunications networks and multimodal transport. It is, given an undirected graph whose links are labeled, and an integer positive number k, find the spanning forest with the lowest number of connected components using at most k different labels. To address the problem a Basic Variable Neighbourhood Search is proposed where the maximum amplitude of the neighbourhood space, n, is a key parameter. Different strategies are studied to establish the value of n. BVNS with the best selected strategy is experimentally compared with other metaheuristics that have appeared in the literature applied to this type of problem.

cs.DM↗
arXiv2014

Mejora de la exploracion y la explotacion de las heuristicas constructivas para el MLSTP

This paper 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.

cs.DM↗
arXiv2014

Solving the minimum labelling spanning tree problem using intelligent optimization

Given a connected, undirected graph whose edges are labelled (or coloured), the minimum labelling spanning tree (MLST) problem seeks a spanning tree whose edges have the smallest number of distinct labels (or colours). In recent work, the MLST problem has been shown to be NP-hard and some effective heuristics have been proposed and analyzed. In this paper we present an intelligent optimization algorithm to solve the problem. It is obtained by the basic Variable Neighbourhood Search heuristic with the integration of other complements from machine learning, statistics and experimental algorithmics, in order to produce high-quality performance and to completely automate the resulting optimization strategy. We present experimental results on randomly generated graphs with different statistical properties, showing the crucial effects of the implementation, the robustness, and the empirical scalability of our intelligent algorithm. Furthermore, the computational experiments show that the proposed strategy outperforms the heuristics recommended in the literature and is able to obtain optimal or near-optimal solutions in short computational running time.

math.OC↗
arXiv2014

A survey on tidal analysis and forecasting methods for Tsunami detection

Accurate analysis and forecasting of tidal level are very important tasks for human activities in oceanic and coastal areas. They can be crucial in catastrophic situations like occurrences of Tsunamis in order to provide a rapid alerting to the human population involved and to save lives. Conventional tidal forecasting methods are based on harmonic analysis using the least squares method to determine harmonic parameters. However, a large number of parameters and long-term measured data are required for precise tidal level predictions with harmonic analysis. Furthermore, traditional harmonic methods rely on models based on the analysis of astronomical components and they can be inadequate when the contribution of non-astronomical components, such as the weather, is significant. Other alternative approaches have been developed in the literature in order to deal with these situations and provide predictions with the desired accuracy, with respect also to the length of the available tidal record. These methods include standard high or band pass filtering techniques, although the relatively deterministic character and large amplitude of tidal signals make special techniques, like artificial neural networks and wavelets transform analysis methods, more effective. This paper is intended to provide the communities of both researchers and practitioners with a broadly applicable, up to date coverage of tidal analysis and forecasting methodologies that have proven to be successful in a variety of circumstances, and that hold particular promise for success in the future. Classical and novel methods are reviewed in a systematic and consistent way, outlining their main concepts and components, similarities and differences, advantages and disadvantages.

cs.CE↗