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Traveling Salesman Algorithms Complexity

by Fatima Thaher Aburomman
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 43
Year of Publication: 2019
Authors: Fatima Thaher Aburomman
10.5120/ijca2019918546

Fatima Thaher Aburomman . Traveling Salesman Algorithms Complexity. International Journal of Computer Applications. 182, 43 ( Mar 2019), 29-30. DOI=10.5120/ijca2019918546

@article{ 10.5120/ijca2019918546,
author = { Fatima Thaher Aburomman },
title = { Traveling Salesman Algorithms Complexity },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2019 },
volume = { 182 },
number = { 43 },
month = { Mar },
year = { 2019 },
issn = { 0975-8887 },
pages = { 29-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number43/30439-2019918546/ },
doi = { 10.5120/ijca2019918546 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:14:10.424192+05:30
%A Fatima Thaher Aburomman
%T Traveling Salesman Algorithms Complexity
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 43
%P 29-30
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The travelling salesman problem (TSP) is widely studied in computer science. There is a practical importance, and can be applied to solve many practical daily lives problems, so many algorithms developed to solve this problem, each with its efficient. Insertion, genetic, greedy, greedy 2-opts and nearest neighbor, are all algorithms used to solve (TSP). This paper will study these algorithms and present the main differences between these algorithms according to its complexity, and which one is the most efficient to solve the (TSP)

References
  1. Abdullah Homaitor Shanyunchauna,and Gunar E.liepins.Scherna analysis of the travelling salesman problem using genetic algorithms,complex system.
  2. Fisher,R,Richter,K..Solving amulti objective Travelling Sales man problem by dynamic programming.
  3. Hansen,M.Puse of substitute Scalarizing functions to guide alocal search based heuristics the case of MOTSP.Journal of Heuristics6(2000)419-431
  4. Yan,Zhang L.kang Anew MOEA for multi-objective TSP and it’s convergence property analysis.
  5. Alba, E. Parallel Metaheuristics: A New Class of Algorithms. Wiley Series on Parallel and Distributed Computing. Wiley-Interscience, Hoboken, NJ, 2005.
  6. Gutin G., A. Punnen. The Traveling Salesman Problem and Its Variations, Kluwer Academic Publishers, 2004.
  7. Reeves C., J. Rowe, Genetic Algorithms . Principles and Perspectives: A Guide to GA Theory, Springer, 2002.
  8. G. Bendall and F. Margot, Greedy Type Resistance of Combinatorial Problems, Discrete Optimization 3 (2006), 288298.
  9. P. H. Chen, S. Malkani, C.-M. Peng, and J. Lin. Fixing antenna problem by dynamic diode dropping and jumper insertion. In Proc. of ISQED, 2000.
Index Terms

Computer Science
Information Sciences

Keywords

TSP complexity using Insertion TSP using Greedy TSP using Genetic

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