Dynamic Shortest Path Algorithms for Hypergraphs

Gao, Jianhang; Zhao, Qing; Ren, Wei; Swami, Ananthram; Ramanathan, Ram; Bar-Noy, Amotz

doi:10.1109/tnet.2014.2343914

Cited by 43 publications

(23 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These algorithms are very inefficient when the occurrence of link state changes in a network. Some other methods maintain shortest paths in Hypergraphs 33 , in planar graphs 32,34 , some require un-weighted graphs 35 , and some permit only integer edge weights 36,37 , and its variant 38 . Many other shortest path problems have been solved by using different approaches 39,40,40,41,42,43 .…”

Section: Dsprpmentioning

confidence: 99%

A Reduced Uncertainty-Based Hybrid Evolutionary Algorithm for Solving Dynamic Shortest-Path Routing Problem

Kusetoğulları

Sharif

Leeson

et al. 2015

J CIRCUIT SYST COMP

View full text Add to dashboard Cite

A reduced-uncertainty hybrid evolutionary algorithm for solving dynamic shortest-path routing problem. Journal of Circuits, Systems and Computers, 24 (5). Permanent WRAP url:http://wrap.warwick.ac.uk/73771 Copyright and reuse:The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.Copies of full items can be used for personal research or study, educational, or not-for profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. The need of effective packet transmission to deliver advanced performance in wireless networks creates the need to find shortest network paths efficiently and quickly. This paper addresses a Reduced Uncertainty Based Hybrid Evolutionary Algorithm (RUBHEA) to solve Dynamic Shortest Path Routing Problem (DSPRP) effectively and rapidly. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are integrated as a hybrid algorithm to find the best solution within the search space of dynamically changing networks. Both GA and PSO share context of individuals to reduce uncertainty in RUBHEA. Various regions of search space are explored and learned by RUBHEA. By employing a modified priority encoding method, each individual in both GA and PSO are represented as a potential solution for DSPRP. A Complete statistical analysis has been performed to compare the performance of RUBHEA with various state-of-the-art algorithms. It shows that RUBHEA is considerably superior (reducing the failure rate by up to 50%) to similar approaches with increasing number of nodes encountered in the networks.

show abstract

Section: Dsprpmentioning

confidence: 99%

A Reduced Uncertainty-Based Hybrid Evolutionary Algorithm for Solving Dynamic Shortest-Path Routing Problem

Kusetoğulları

Sharif

Leeson

et al. 2015

J CIRCUIT SYST COMP

View full text Add to dashboard Cite

show abstract

“…Further, in the literature, many variations and special cases of the shortest path problem were proposed. For instance, we refer to the recent works on massively parallel systems [5], dynamic algorithms for hypergraphs [12], random networks with uncertain arc lengths [13,25] and in fuzzy graphs [9,11]. There are numerous algorithms and techniques proposed by many researchers; see [1,4,10,15,19,[22][23][24] for probabilisitic approach and for large graphs, we refer to [20,21], and [29] exploits the symmetry in graphs, for more related literature.…”

Section: Introductionmentioning

confidence: 99%

A novel graph matrix representation: sequence of neighbourhood matrices with an application

Karunakaran

Selvaganesh

2020

SN Appl. Sci.

View full text Add to dashboard Cite

In the study of network optimization, finding the shortest path minimizing time/distance/cost from a source node to a destination node is one of the fundamental problems. Our focus here is to find the shortest path between any pair of nodes in a given undirected unweighted simple graph with the help of the sequence of powers of neighbourhood matrices. The authors recently introduced the concept of neighbourhood matrix as a novel representation of graphs using the neighbourhood sets of the vertices. In this article, an extension of the above work is presented by introducing a sequence of matrices, referred to as the sequence of powers of NM(G). It is denoted it by NM {l} (G) = [ {l} ij ], 1 ≤ l ≤ k(G) , where k(G) is called the iteration number, k(G) = ⌈log 2 diameter(G)⌉. As this sequence of matrices captures the distance between the nodes profoundly, we further develop the technique and present several characterizations. Based on the theoretical results, we present an algorithm to find the shortest path between any pair of nodes in a given graph. The proposed algorithm and the claims therein are formally validated through simulations on synthetic data and the real network data from Facebook. The empirical results are quite promising with our algorithm having best running time among all the existing well-known shortest path algorithms for the considered graph classes.

show abstract

“…There are two types of graph; directed graph for which each node is directed by one way to any other node, and undirected graph for which its possible to go to and back from the same way between two connected nodes. The issue of the shortest path problem is related to graph theory, which is one of the most important topics for researchers [1,2,3,4,5,6,7]. It concerns with finding the shortest path between two nodes, or between a node as a source to all other nodes, depending on the weights of the edges that link these nodes [8].…”

Section: Introductionmentioning

confidence: 99%

“…They are also exploring more various ways and methods to get the lowest cost of path into a large number of nodes and speeding up their search process. Some of them are working on reconstructing the graphs in an attempt to reduce the cost of searching time either by 34 Computer Science & Information Technology (CS & IT) compression, optimization or subgraphs [1,2,3,4,5,6,15]. Others are replacing data structure with another [18,19,20,21].…”

Section: Introductionmentioning

confidence: 99%

The Implicit Path Cost Optimization in Dijkstra Algorithm Using Hash Map Data Structure

Amarif¹,

Alashoury²

2019

Computer Science &Amp; Information Technology(CS &Amp; IT)

View full text Add to dashboard Cite

The shortest path between two points is one of the greatest challenges facing the researchers nowadays. There are many algorithms and mechanisms that are designed and still all according to the certain approach and adopted structural. The most famous and widely used algorithm is Dijkstra algorithm, which is characterized by finding the shortest path between two points through graph data structure. It's obvious to find the implicit path from the solution path; but the searching time varies according to the type of data structure used to store the solution path. This paper improves the development of Dijkstra algorithm using linked hash map data structure for storing the produced solution shortest path, and then investigates the subsequent implicit paths within this data structure. The result show that the searching time through the given data structure is much better than restart the algorithm again to search for the same path.

show abstract

Dynamic Shortest Path Algorithms for Hypergraphs

Cited by 43 publications

References 16 publications

A Reduced Uncertainty-Based Hybrid Evolutionary Algorithm for Solving Dynamic Shortest-Path Routing Problem

A Reduced Uncertainty-Based Hybrid Evolutionary Algorithm for Solving Dynamic Shortest-Path Routing Problem

A novel graph matrix representation: sequence of neighbourhood matrices with an application

The Implicit Path Cost Optimization in Dijkstra Algorithm Using Hash Map Data Structure

Contact Info

Product

Resources

About