IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

T-Spanner Problem: Genetic Algorithms for the T-Spanner Problem

T-Spanner Problem: Genetic Algorithms for the T-Spanner Problem
View Sample PDF
Author(s): Riham Moharam (Suez Canal University, Egypt), Ehab Morsy (Suez Canal University, Egypt)and Ismail A. Ismail (6 October University, Egypt)
Copyright: 2021
Pages: 20
Source title: Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-8048-6.ch019

Purchase

View T-Spanner Problem: Genetic Algorithms for the T-Spanner Problem on the publisher's website for pricing and purchasing information.

Abstract

The t-spanner problem is a popular combinatorial optimization problem and has different applications in communication networks and distributed systems. This chapter considers the problem of constructing a t-spanner subgraph H in a given undirected edge-weighted graph G in the sense that the distance between every pair of vertices in H is at most t times the shortest distance between the two vertices in G. The value of t, called the stretch factor, quantifies the quality of the distance approximation of the corresponding t-spanner subgraph. This chapter studies two variations of the problem, the Minimum t-Spanner Subgraph (MtSS) and the Minimum Maximum Stretch Spanning Tree(MMST). Given a value for the stretch factor t, the MtSS problem asks to find the t-spanner subgraph of the minimum total weight in G. The MMST problem looks for a tree T in G that minimizes the maximum distance between all pairs of vertices in V (i.e., minimizing the stretch factor of the constructed tree). It is easy to conclude from the literatures that the above problems are NP-hard. This chapter presents genetic algorithms that returns a high quality solution for those two problems.

Related Content

Shailendra Aote, Mukesh M. Raghuwanshi. © 2021. 34 pages.
Anjana Mishra, Bighnaraj Naik, Suresh Kumar Srichandan. © 2021. 15 pages.
Thendral Puyalnithi, Madhuviswanatham Vankadara. © 2021. 15 pages.
Geng Zhang, Xiansheng Gong, Xirui Chen. © 2021. 13 pages.
Jhuma Ray, Siddhartha Bhattacharyya, N. Bhupendro Singh. © 2021. 19 pages.
Pijush Samui, Viswanathan R., Jagan J., Pradeep U. Kurup. © 2021. 18 pages.
Ravinesh C. Deo, Sujan Ghimire, Nathan J. Downs, Nawin Raj. © 2021. 32 pages.
Body Bottom