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

Authors

  • Savita R Dept. Of Computer Science & Engineering, Vikrant Institute of Technology & Management, Gwalior, India
  • Sharma P Dept. Of Computer Science & Engineering, Vikrant Institute of Technology & Management, Gwalior, India
  • Gupta M Dept. Of Computer Science & Engineering, Vikrant Institute of Technology & Management, Gwalior, India

DOI:

https://doi.org/10.26438/ijcse/v7i1.267270

Keywords:

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

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.

References

[1] L. Shufen, L. Huang and H. Lu,” Pheromone Model Selection in Ant Colony Optimization for the Travelling Salesman Problem”, Chinese Journal of Electronics, Vol.26, No.2, Mar. 2017.

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

[3] N. Xiong, W. Wu and C. Wu,” An Improved Routing Optimization Algorithm Based on Travelling Salesman Problem for Social Networks” Sustainability 2017, 9, 985; doi:10.3390/su9060985.

[4] Z. A. Aziz,” Ant Colony Hyper-heuristics for Travelling Salesman Problem”, IEEE International Symposium on Robotics and Intelligent Sensors (IRIS 2015), Procedia Computer Science 76 ( 2015 ) 534 – 538.

[5] Jiang, Y. ,”The Application of an Improved Ant Colony Optimization for TSP”, South-central University for Nationality: Wuhan, China, 2009.

[6] Chen, W.; Jiang, Y.,” Improving ant colony algorithm and particle swarm algorithm to solve TSP problem”, Inf. Technol. 2016, 2016, 162–165.

[7] Wang, Z.; Bai, Y.; Yue, L.,” An Improved Ant Colony Algorithm for Solving TSP Problems”, Math. Pract. Theory 2012, 42, 133–140.

[8] Sun, J.,” Research on Ant Colony Algorithm for Solving Travelling Salesman Problem”, Wuhan University of Technology: Wuhan, China, 2005.

Downloads

Published

2019-01-31
CITATION
DOI: 10.26438/ijcse/v7i1.267270
Published: 2019-01-31

How to Cite

[1]
R. Savita, P. Sharma, and M. Gupta, “An Improved Version of Update Pheromone Rule of ACO algorithm for TSP”, Int. J. Comp. Sci. Eng., vol. 7, no. 1, pp. 267–270, Jan. 2019.

Issue

Vol. 7 No. 1 (2019): IJCSE January Edition

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.