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An Improved Version of Update Pheromone Rule of ACO algorithm for TSP

Ranjeet Savita1 , Pankaj Sharma2 , Manish Gupta3

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-1 , Page no. 267-270, Jan-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i1.267270

Online published on Jan 31, 2019

Copyright © Ranjeet Savita, Pankaj Sharma, Manish Gupta . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Ranjeet Savita, Pankaj Sharma, Manish Gupta, “An Improved Version of Update Pheromone Rule of ACO algorithm for TSP,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.267-270, 2019.

MLA Style Citation: Ranjeet Savita, Pankaj Sharma, Manish Gupta "An Improved Version of Update Pheromone Rule of ACO algorithm for TSP." International Journal of Computer Sciences and Engineering 7.1 (2019): 267-270.

APA Style Citation: Ranjeet Savita, Pankaj Sharma, Manish Gupta, (2019). An Improved Version of Update Pheromone Rule of ACO algorithm for TSP. International Journal of Computer Sciences and Engineering, 7(1), 267-270.

BibTex Style Citation:
@article{Savita_2019,
author = {Ranjeet Savita, Pankaj Sharma, Manish Gupta},
title = {An Improved Version of Update Pheromone Rule of ACO algorithm for TSP},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {1 2019},
volume = {7},
Issue = {1},
month = {1},
year = {2019},
issn = {2347-2693},
pages = {267-270},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3495},
doi = {https://doi.org/10.26438/ijcse/v7i1.267270}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i1.267270}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3495
TI - An Improved Version of Update Pheromone Rule of ACO algorithm for TSP
T2 - International Journal of Computer Sciences and Engineering
AU - Ranjeet Savita, Pankaj Sharma, Manish Gupta
PY - 2019
DA - 2019/01/31
PB - IJCSE, Indore, INDIA
SP - 267-270
IS - 1
VL - 7
SN - 2347-2693
ER -

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Abstract

Ant colony optimization algorithm is a popular meta-heuristic optimization algorithm that has been proven successful for solving travelling salesman problem. In this paper, modified version of ant colony optimization for solving travelling salesman problem has been proposed. In this modified version, update pheromone phase of ant colony optimization algorithm is updated. Here, best distance is calculated by comparing all the nodes distance and taken the best distance for find next node instead of taking ants one by one and keep updating later on. This modified version improves the total cost as well as total time of travelling salesman problem. Proposed algorithm is performed on 51 cities, 61 cities, 70 cities and 76 cities problem. Comparative study shows that proposed algorithm is better than standard ant colony optimization algorithm.

Key-Words / Index Term

Ant colony optimization, Travelling salesman problem, ACO, TSP, Update Pheromone Phase

References

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[2] D. M. Chitty,” Applying ACO to Large Scale TSP Instances,” UK Workshop on Computational Intelligence, pp. 104-118. Springer, Cham, 2017.
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