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.
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References
M. Dorigo, T. Stutzle, Ant Colony Optimization, The MIT Press, Cambridge, 2004.
M. Dorigo, V. Maniezzo, A. Colorni, Positive Feedback as a Search Strategy (Technical Report No. 91-016), Politecnico di Milano, Milano, Italy, 1991.
M. Dorigo, V. Maniezzo, A. Colorni, Ant system: optimization by a colony of cooperating agents, IEEE Trans. Syst. Man. Cybern. B. 26 (1996), 29–41.
M. Dorigo, L.M. Gambardella, Ant colony system: a cooperative learning approach to the traveling salesman problem, IEEE Trans. Evol. Comput. 1 (1997), 53–66.
B. Bullnheimer, R. Hartl, C. Strauss, A new rank-based version of the ant system: a computational study, Cent. Eur. J. Oper. Res. 7 (1999), 25–38.
T. Stützle, H.H. Hoos, Max-min ant system, Future Gener. Comput. Syst. 16 (2000), 889–914.
Z.J. Zhang, Z.R. Feng, A novel max-min ant system algorithm for traveling salesman problem, in Proceedings of Intelligent Computing and Intelligent Systems (ICIS 2009), IEEE Press, Piscataway, 2009, pp. 508–511.
H.B. Mei, J. Wang, Z.H. Ren, An adaptive dynamic ant system based on acceleration for TSP, in Proceedings of Computational Intelligence and Security, IEEE Press, Piscataway, 2009, pp. 92–96.
P. Guo, Z.J. Liu, Moderate ant system: an improved algorithm for solving TSP, in Proceedings of Seventh International Conference on Natural Computation, IEEE Press, Piscataway, 2011, pp. 1190–1196.
Z.C.S.S. Hlaing, M.A. Khine, Solving traveling salesman problem by using improved ant colony optimization algorithm, Int. J. Inf. Educ. Technol. 1 (2011), 404–409.
G. Dong, W.W. Guo, K. Tickle, Solving the traveling salesman problem using cooperative genetic ant systems, Expert Syst. Appl. 39 (2012), 5006–5011.
A.Q. Ansari, Ibraheem, S. Katiyar, Comparison and analysis of solving travelling salesman problem using GA, ACO and hybrid of ACO with GA and CS, in Proceedings of IEEE Workshop on Computational Intelligence: Theories, Applications and Future Directions, IEEE Press, Piscataway, 2015, pp. 1–5.
Y. Wang, Hybrid max-min ant system with four vertices and three lines inequality for traveling salesman problem, Soft Comput. 19 (2015), 585–596.
Y. Yan, H.S. Sohn, G. Reyes, A modified ant system to achieve better balance between intensification and diversification for the traveling salesman problem, Appl. Soft Comput. 60 (2017), 256–267.
J.C. Thill, Y.C. Kuo, The nearest neighbor ant colony system: a spatially-explicit algorithm for the traveling salesman problem, in: J.-C. Thill (Ed.), Spatial Analysis and Location Modeling in Urban and Regional Systems. Advances in Geographic Information Science, Springer, Berlin, Heidelberg, 2018, pp. 301–322.
G.M. Jaradat, Hybrid elitist-ant system for a symmetric traveling salesman problem: case of Jordan, Neural Comput. Appl. 29 (2018), 565–578.
Ş. Gülcü, M. Mahi, Ö.K. Baykan, et al., A parallel cooperative hybrid method based on ant colony optimization and 3-opt algorithm for solving traveling salesman problem, Soft Comput. 22 (2018), 1669–1685.
X.M. You, S. Liu, Y.M. Wang, Quantum dynamic mechanism-based parallel ant colony optimization algorithm, Int. J. Comput. Int. Sys. 3 (2010), 101–113.
J. Bang, J. Ryu, C. Lee, et al., A quantum heuristic algorithm for the traveling salesman problem, J. Korean Phys. Soc. 61 (2012), 1944–1949.
M.S. Kıran, H. İşcan, M. Gündüz, The analysis of discrete artificial bee colony algorithm with neighborhood operator on traveling salesman problem, Neural Comput. Appl. 23 (2013), 9–21.
J. Jones, A. Adamatzky, Computation of the travelling salesman problem by a shrinking blob, Nat. Comput. 13 (2014), 1–16.
A. Ouaarab, B. Ahiod, X.S. Yang, Discrete cuckoo search algorithm for the travelling salesman problem, Neural Comput. Appl. 24 (2014), 1659–1669.
A. Ouaarab, B. Ahiod, X.S. Yang, Random-key cuckoo search for the travelling salesman problem, Soft Comput. 19 (2015), 1099–1106.
J.B. Odili, M.N.M. Kahar, Solving the traveling salesman’s problem using the African buffalo optimization, Comput. Intell. Neurosci. 2016 (2016), 1510256.
Y. Saji, M.E. Riffi, A novel discrete bat algorithm for solving the travelling salesman problem, Neural Comput. Appl. 27 (2016), 1853–1866.
L. Huang, G.C. Wang, T. Bai, et al., An improved fruit fly optimization algorithm for solving traveling salesman problem, Front. Inform. Tech. El. Eng. 18 (2017), 1525–1533.
M.M. Alipour, S.N. Razavi, M.R.F. Derakhshi, et al., A hybrid algorithm using a genetic algorithm and multiagent reinforcement learning heuristic to solve the traveling salesman problem, Neural Comput. Appl. 30 (2018), 2935–2851.
A.E. Yildirim, A. Karci, Applications of artificial atom algorithm to small-scale traveling salesman problems, Soft Comput. 22 (2018), 7619–7631.
Y.Q. Zhou, R. Wang, C.Y. Zhao, et al., Discrete greedy flower pollination algorithm for spherical traveling salesman problem, Neural Comput. Appl. 31 (2019), 2155–2170.
M.H. Chen, S.H. Chen, P.C. Chang, Imperial competitive algorithm with policy learning for the traveling salesman problem, Soft Comput. 21 (2017), 1863–1875.
A. Hatamlou, Solving travelling salesman problem using black hole algorithm, Soft Comput. 22 (2018), 8167–8175.
A.E.S. Ezugwu, A.O. Adewumi, M.E. Frîncu, Simulated annealing based symbiotic organisms search optimization algorithm for traveling salesman problem, Expert Syst. Appl. 77 (2017), 189–210.
A.E.S. Ezugwu, A.O. Adewumi, Discrete symbiotic organisms search algorithm for travelling salesman problem, Expert Syst. Appl. 87 (2017), 70–78.
Y.W. Zhong, J. Lin, L.J. Wang, et al., Hybrid discrete artificial bee colony algorithm with threshold acceptance criterion for traveling salesman problem, Inf. Sci. 421 (2017), 70–84.
S. Kumar, E. Munapo, M. Lesaoana, et al., A minimum spanning tree based heuristic for the travelling salesman tour, Opsearch. 55 (2018), 150–164.
K.M. Lo, W.Y. Yi, P.K. Wong, et al., A genetic algorithm with new local operators for multiple traveling salesman problems, Int. J. Comput. Int. Sys. 11 (2018), 692–705.
R.Y. Dong, S.S. Wang, G.Y. Wang, et al., Hybrid optimization algorithm based on wolf pack search and local search for solving traveling salesman problem, J. Shanghai Jiao Tong Univ. 24 (2019), 41–47.
M.A.H. Akhanda, S.I. Ayon, S.A. Shahriyar, et al., Discrete spider monkey optimization for traveling salesman problem. Appl. Soft Comput. 86 (2020), 105887.
Y.W. Zhong, L.J. Wang, M. Lin, et al., Discrete pigeon-inspired optimization algorithm with metropolis acceptance criterion for large-scale traveling salesman problem, Swarm Evol. Comput. 48 (2019), 134–144.
C. Jiang, Z.P. Wan, Z.H. Peng. A new efficient hybrid algorithm for large scale multiple traveling salesman problems, Expert Syst. Appl. 139 (2020), 112867.
B. Christian, Ant colony optimization: introduction and recent trends, Phys. Life Rev. 2 (2005), 353–373.
M.B. Chandra, R. Baskaran, Survey on recent research and implementation of ant colony optimization in various engineering applications, Int. J. Comput. Int. Sys. 4 (2011), 566–582.
Y. Nakamichi, T. Arita, Diversity control in ant colony optimization, Artif. Life Robot. 7 (2004), 198–204.
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Gao, W. New Ant Colony Optimization Algorithm for the Traveling Salesman Problem. Int J Comput Intell Syst 13, 44–55 (2020). https://doi.org/10.2991/ijcis.d.200117.001
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DOI: https://doi.org/10.2991/ijcis.d.200117.001