International Journal of Computational Intelligence Systems

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Volume 13, Issue 1, 2020, Pages 44 - 55

New Ant Colony Optimization Algorithm for the Traveling Salesman Problem

Authors
Wei Gao*
College of Civil and Transportation Engineering, Hohai University, Nanjing, Jiangsu 210098, PR China
Corresponding Author
Wei Gao
Received 9 October 2019, Accepted 27 December 2019, Available Online 22 January 2020.
DOI
10.2991/ijcis.d.200117.001How to use a DOI?
Keywords
Computational intelligence optimization; New ant colony optimization algorithm; Meeting strategy; Performance; Traveling salesman problem
Abstract

As one suitable optimization method implementing computational intelligence, ant colony optimization (ACO) can be used to solve the traveling salesman problem (TSP). However, traditional ACO has many shortcomings, including slow convergence and low efficiency. By enlarging the ants' search space and diversifying the potential solutions, a new ACO algorithm is proposed. In this new algorithm, to diversify the solution space, a strategy of combining pairs of searching ants is used. Additionally, to reduce the influence of having a limited number of meeting ants, a threshold constant is introduced. Based on applying the algorithm to 20 typical TSPs, the performance of the new algorithm is verified to be good. Moreover, by comparison with 16 state-of-the-art algorithms, the results show that the proposed new algorithm is a highly suitable method to solve the TSP, and its performance is better than those of most algorithms. Finally, by solving eight TSPs, the good performance of the new algorithm has been analyzed more comprehensively by comparison with that of the typical traditional ACO. The results show that the new algorithm can attain a better solution with higher accuracy and less effort.

Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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<Previous Article In Issue
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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
13 - 1
Pages
44 - 55
Publication Date
2020/01/22
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.200117.001How to use a DOI?
Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Wei Gao
PY  - 2020
DA  - 2020/01/22
TI  - New Ant Colony Optimization Algorithm for the Traveling Salesman Problem
JO  - International Journal of Computational Intelligence Systems
SP  - 44
EP  - 55
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200117.001
DO  - 10.2991/ijcis.d.200117.001
ID  - Gao2020
ER  -