The Minimum Routing Cost Tree Problem

Masone, Adriano; Nenni, Maria Elena; Sforza, Antonio; Sterle, Claudio

doi:10.1007/s00500-018-3557-3

Cited by 10 publications

(7 citation statements)

References 20 publications

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“…Only substations and disconnectors can be automated and, therefore, the network is characterized by 24 potential IED locations (|S| = 24). As said above, the considered network has a tree structure, and therefore the nodes are connected by 40 sections of different lengths [22]. We assumed a one-to-one correspondence between sections and failure scenarios; therefore, |F| = 40 and each scenario represent a section failure.…”

Section: Assessment On a Real MV Gridmentioning

confidence: 99%

Optimization of IEDs Position in MV Smart Grids through Integer Linear Programming

Bonavolontà

Caragallo²,

Fatica³

et al. 2021

Energies

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In the paper, an analytical method for determining the optimal positioning of intelligent electronic devices in medium voltage grids is proposed. Intelligent electronic devices are automated devices able to communicate one with each other and command the circuit breaker in order to localize and isolate a line fault as fast as possible. However, the number of intelligent electronic devices to install has to be limited, due to the relevant installation costs and the reduction in the transmission bandwidth caused by the increased number of exchanged messages. So, the electrical distributor has to carefully detect the nodes of the grid where the intelligent electronic devices have to be installed. The authors propose a method based on integer linear programming, which, given the number of intelligent electronic devices to install, finds their optimal position, i.e., the one that minimizes the penalties associated with the power down experienced by customers. In order to highlight the offered advantages in terms of computational effort, the proposed approach has been assessed with a real medium voltage grid.

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Section: Assessment On a Real MV Gridmentioning

confidence: 99%

Optimization of IEDs Position in MV Smart Grids through Integer Linear Programming

Bonavolontà

Caragallo²,

Fatica³

et al. 2021

Energies

Self Cite

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“…We use a heuristic algorithm based on the identification of a set of nodes around where the solution can be built. This end is called the core of the network [36]. Based on opportunely weighted-out degree values, we classify the nodes in several levels.…”

Section: Active Node Selectionmentioning

confidence: 99%

A Heuristic Algorithm of Cooperative Agents Communication for Enhanced GAF Routing Protocol in WSNs

Aznaoui

Raghay

Ouakrim

et al. 2019

Wireless Communications and Mobile Computing

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Rapid progress in technologies has led to the development of small sensor nodes. A wireless sensor network (WSN) is an interconnected collection of a large number of these small sensor nodes that is used to monitor and record the physical environment. WSNs have applications in diverse scenarios. They play an important role in tracking and monitoring in different domains, such as environmental research, military, and health care. In most of these applications, the WSN is composed of a large number of nodes deployed in an area of interest, and not all nodes are directly connected to the base station (BS). In some cases, batteries of nodes cannot be recharged or changed. For that, the most solution required to overcome these problems is to optimize energy consumed during communication. Data transmission in networks is maintained by routing protocols, which are responsible for discovering the required paths. This paper presents an improvement of the Geographic Adaptive Fidelity (GAF) routing protocol created on a smart actives node selection. The routing process works on cooperative agents communication where another node is activated in the same grid if the data collected are considered as important data, and a heuristic method is used to find an optimal path in terms of energy to transmit data collected until reaching the BS. Simulation results prove that the cooperative agents GAF (CAGAF) routing protocol proposed is more efficient compared to the basic version in terms of considering important data, energy consumed, and dead nodes.

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“…The class of problems related to finding minimum cost clustered trees is among the most widely studied problems in applied mathematics and theoretical computer science. In general, clustered tree problems have many applications in the field of computer network design, computational biology, transportation and logistics as well as water resource management [12,13]. Numerous problems with different cost function has received much attention such as Clustered Minimum Steiner Tree [21], Clustered Shortest-Path Tree [7] and Minimum Routing Cost Clustered Tree Problem (CluMRCT) [12].…”

Section: Introductionmentioning

confidence: 99%

Multifactorial Evolutionary Algorithm For Clustered Minimum Routing Cost Problem

Trung

Thanh

Hieu

et al. 2019

Proceedings of the Tenth International Symposium on Information and Communication Technology - SoICT 2019

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Minimum Routing Cost Clustered Tree Problem (CluMRCT) is applied in various fields in both theory and application. Because the CluMRCT is NP-Hard, the approximate approaches are suitable to find the solution for this problem. Recently, Multifactorial Evolutionary Algorithm (MFEA) has emerged as one of the most efficient approximation algorithms to deal with many different kinds of problems. Therefore, this paper studies to apply MFEA for solving CluMRCT problems. In the proposed MFEA, we focus on crossover and mutation operators which create a valid solution of CluM-RCT problem in two levels: first level constructs spanning trees for graphs in clusters while the second level builds a spanning tree for connecting among clusters. To reduce the consuming resources, we will also introduce a new method of calculating the cost of CluMRCT solution. The proposed algorithm is experimented on numerous types of datasets. The experimental results demonstrate the effectiveness of the proposed algorithm, partially on large instances. [12].Among those mentioned problems, CluMRCT is one of the most newly investigated problems. This problem has been formally formulated in [12]. Concretely, consider a connected, undirected graph G = (V , E, w) with nonnegative edge length function w :A spanning tree T of G is a clustered spanning tree if T can be cut into k subtrees by eliminating k − 1 edges such that each subtree is a spanning tree for only one cluster. The CluMRCT problem focuses on the routing cost, which is the sum of shortest path distance between any pairs of vertices given a clustered spanning tree T . The CluMRCT problem is finding on graph G a clustered spanning tree T having the minimum routing cost. One practical realization of this problem is in computer network application where the communication terminals are vertices, and they are partitioned into many clusters. The communication between those terminals are restricted within a cluster and only a few terminals can be connected to another cluster for maximizing efficiency and other security concerns. Solving CluMRCT is equivalent to facilitate the network architecture that consumes the minimum peer-to-peer communication resources.

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The Minimum Routing Cost Tree Problem

Cited by 10 publications

References 20 publications

Optimization of IEDs Position in MV Smart Grids through Integer Linear Programming

Optimization of IEDs Position in MV Smart Grids through Integer Linear Programming

A Heuristic Algorithm of Cooperative Agents Communication for Enhanced GAF Routing Protocol in WSNs

Multifactorial Evolutionary Algorithm For Clustered Minimum Routing Cost Problem

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