Comparison between ant colony and genetic algorithm using traveling salesman problem

The Travelling Salesman Problem (TSP) is a complex problem in combinatorial optimization. The aim of this study is compare the effect of using two distributed algorithm which are ant colony as a Swarm intelligence algorithm and genetic algorithm. In ant colony algorithm each individual ant construct...

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Main Authors: Abduljabbar, Zaid Ammen, Khalefa, Mustafa S., A. Jabar, Marzanah
Format: Article
Language:English
Published: Medwell Publishing 2013
Online Access:http://psasir.upm.edu.my/id/eprint/30686/
http://psasir.upm.edu.my/id/eprint/30686/1/Comparison%20between%20ant%20colony%20and%20genetic%20algorithm%20using%20traveling%20salesman%20problem.pdf
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author Abduljabbar, Zaid Ammen
Khalefa, Mustafa S.
A. Jabar, Marzanah
author_facet Abduljabbar, Zaid Ammen
Khalefa, Mustafa S.
A. Jabar, Marzanah
author_sort Abduljabbar, Zaid Ammen
building UPM Institutional Repository
collection Online Access
description The Travelling Salesman Problem (TSP) is a complex problem in combinatorial optimization. The aim of this study is compare the effect of using two distributed algorithm which are ant colony as a Swarm intelligence algorithm and genetic algorithm. In ant colony algorithm each individual ant constructs a part of the solution using an artificial pheromone which reflects its experience accumulated while solving the problem and heuristic information dependent on the problem. The results of comparison show that ant colony is high efficient than genetic algorithm and it requires less computational cost and generally only a few lines of code.
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spelling upm-306862016-06-30T06:31:29Z http://psasir.upm.edu.my/id/eprint/30686/ Comparison between ant colony and genetic algorithm using traveling salesman problem Abduljabbar, Zaid Ammen Khalefa, Mustafa S. A. Jabar, Marzanah The Travelling Salesman Problem (TSP) is a complex problem in combinatorial optimization. The aim of this study is compare the effect of using two distributed algorithm which are ant colony as a Swarm intelligence algorithm and genetic algorithm. In ant colony algorithm each individual ant constructs a part of the solution using an artificial pheromone which reflects its experience accumulated while solving the problem and heuristic information dependent on the problem. The results of comparison show that ant colony is high efficient than genetic algorithm and it requires less computational cost and generally only a few lines of code. Medwell Publishing 2013 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/30686/1/Comparison%20between%20ant%20colony%20and%20genetic%20algorithm%20using%20traveling%20salesman%20problem.pdf Abduljabbar, Zaid Ammen and Khalefa, Mustafa S. and A. Jabar, Marzanah (2013) Comparison between ant colony and genetic algorithm using traveling salesman problem. International Journal of Soft Computing, 8 (3). pp. 171-174. ISSN 1816-9503 http://www.medwelljournals.com/abstract/?doi=ijscomp.2013.171.174
spellingShingle Abduljabbar, Zaid Ammen
Khalefa, Mustafa S.
A. Jabar, Marzanah
Comparison between ant colony and genetic algorithm using traveling salesman problem
title Comparison between ant colony and genetic algorithm using traveling salesman problem
title_full Comparison between ant colony and genetic algorithm using traveling salesman problem
title_fullStr Comparison between ant colony and genetic algorithm using traveling salesman problem
title_full_unstemmed Comparison between ant colony and genetic algorithm using traveling salesman problem
title_short Comparison between ant colony and genetic algorithm using traveling salesman problem
title_sort comparison between ant colony and genetic algorithm using traveling salesman problem
url http://psasir.upm.edu.my/id/eprint/30686/
http://psasir.upm.edu.my/id/eprint/30686/
http://psasir.upm.edu.my/id/eprint/30686/1/Comparison%20between%20ant%20colony%20and%20genetic%20algorithm%20using%20traveling%20salesman%20problem.pdf