Identification of betweenness-central groups of components in a complex network infrastructure by genetic algorithms

Zio, Enrico; Golea, L. R.

doi:10.1201/9780203859759.ch217

Cited by 6 publications

(4 citation statements)

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“…For example, in a network with 3 K  edges and using 1 In a network with K edges, the number of groups (single edges, pairs, triplets and so forth) that in principle can be formed is 2 K . A complete analysis of all groups to find the most critical is therefore impractical for large networks [Zio and Golea, 2009]. This fact suggests devising appropriate heuristic procedures to solve the problem, as the one based on GA here proposed.…”

Section: Genetic Algorithm For Identifying Critical Groupsmentioning

confidence: 99%

Identifying groups of critical edges in a realistic electrical network by multi-objective genetic algorithms

Zio

Golea

2012

Reliability Engineering & System Safety

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International audienceIn this paper, an analysis of the vulnerability of the Italian high-voltage (380 kV) electrical transmission network (HVIET) is carried out for the identification of the groups of links (or edges, or arcs) most critical considering the network structure and flow. Betweenness centrality and network connection efficiency variations are considered as measures of the importance of the network links. The search of the most critical ones is carried out within a multi-objective optimization problem aimed at the maximization of the importance of the groups and minimization of their dimension. The problem is solved using a genetic algorithm. The analysis is based only on information on the topology of the network and leads to the identification of the most important single component, couples of components, triplets and so forth. The comparison of the results obtained with those reported by previous analyses indicates that the proposed approach provides useful complementary information

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Section: Genetic Algorithm For Identifying Critical Groupsmentioning

confidence: 99%

Identifying groups of critical edges in a realistic electrical network by multi-objective genetic algorithms

Zio

Golea

2012

Reliability Engineering & System Safety

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“…Zio and Golea 16 and Zio et al 17 propose the use of clustering coefficient, to define groups of important edges or nodes. Each group is characterized by an increasing cardinality.…”

Section: Importance Approachmentioning

confidence: 99%

Assessing the importance of components in a system modeled as a network

Ruíz

2012

Proceedings of the Institution of Mechanical Engineers, Part O:

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In this article the importance of network components, such as links or nodes, is analyzed through the evaluation of some ''classical'' and other more recent metrics developed in the literature. In particular, the use of global sensitivity methods is suggested for quantifying the simultaneous effects of components. These assessments allow the decision-maker to identify key components, i.e. the components that affect one or more network performance functions. The article describes each metrics, highlights pro and cons and suggests how to select the most convenient one, based on four aspects: the objective of the decision-maker, the model characteristic, computation time and the information availability. A comparison among different importance measures in two networks is presented.

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“…For example, Rocco et al 6 proposed a multi-objective optimization problem to derive the group of components that, if eliminated from the system, cause the worsening of the global efficiency. Zio and Golea 7 and Zio et al 8 propose two approaches based on the use of topologic metrics derived from the complex system theory, such as clustering coefficients, to define groups of important nodes or edges, respectively. Each group is characterized by an increasing cardinality.…”

Section: Introductionmentioning

confidence: 99%

Network vulnerability assessment via bi-objective optimization with a fragmentation approach as proxy

Yajure¹,

Montilla

Ramírez-Márquez

et al. 2013

Proceedings of the Institution of Mechanical Engineers, Part O:

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The fragmentation of a network is used to understand the effects of element removals on its cohesion. Minimum information is required to fragment a network, namely: the topology of the network. Continuous fragmentation of a network can be used to uncover important/critical elements in the network. This article proposes a bi-objective optimization model that, when solved, provides the most economical network fragmentation strategies for increasing element fragmentation cost. After description and solution of the model, the manuscript describes, via experimentation, how the results of the model can be used as a surrogate metric for understanding element importance performance in real service networks. The experimentation is complemented with a classical example of social network analysis. The results show that the proposed fragmentation models can be used as a guide to identify sets of elements that contribute to the successful performance of a system.

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Identification of betweenness-central groups of components in a complex network infrastructure by genetic algorithms

Cited by 6 publications

References 0 publications

Identifying groups of critical edges in a realistic electrical network by multi-objective genetic algorithms

Identifying groups of critical edges in a realistic electrical network by multi-objective genetic algorithms

Assessing the importance of components in a system modeled as a network

Network vulnerability assessment via bi-objective optimization with a fragmentation approach as proxy

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