Abstract
Most of the meta-heuristic algorithms are based on the natural processes. They were inspired by physical, biological, social, chemical, social–biological, biological–geography, music, and hybrid processes. In this paper, artificial atom algorithm which was inspired by one of natural processes was applied to traveling salesman problem. The obtained results have shown that for small-scale TSP, artificial atom algorithm is closer to optimum than the other compared heuristic algorithms such as tabu search, genetic algorithm, particle swarm optimization, ant colony optimization, and their different combinations.
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Acampora G, Gaeta M, Loia V (2011) Combining multi agent paradigm and memetic computing for personalized and adaptive learning experiences. Comput Intell J 27(2):141–165
Applegate DL, Bixby RE, Chvatal V, Cook WJ (2007) Traveling salesman problem: a computational study. Princeton University Press, New Jersey
Canayaz M, Karci A (2013) A new meta-heuristic cricket-inspired algorithm. In: proceedings of 2nd international eurasian conference on mathematical sciences and applications, Sarajevo, Bosnia and Herzegovina, pp 176
Colak S (2010) An application on the solution of the travelling salesman problem with helping genetic algorithms (in Turkish). C.U. Sosyal Bilimler Enstitusu Dergisi 19(3):423–438
Croes GA (1958) A method for solving traveling salesman problems. Oper Res 6:791–812
Dantzig G, Fulkerson R, Johnson S (1954) Solution of a large-scale travelling salesman problem. Oper Res 2:393–410
Deng W, Chen R, He B, Liu Y, Yin L, Guo J (2012) A novel two-stage hybrid swarm intelligence optimization algorithm and application. Soft Comput 16:1707–1722
Dorigo M, Stutzle T (2004) Ant colony optimization. MIT Press, Cambridge, MA
Ellabib I, Calamai P, Basir O (2007) Exchange strategies for multiple ant colony system. Inf Sci 177(5):1248–1264
Fiechter CN (1994) A parallel tabu search algorithm for large traveling salesman problems. Disc Appl Math 51:243–267
Gavish B, Graves SC (1978) The travelling salesman problem and related problems. Working paper OR-078-78, Opera Res Center, MIT, Cambridge, MA
Goldberg D (1989) Genetic algorithm in search, optimization and machine learning. Addison-Wesley, Massachusetts
Gutin G, Punnen AP (2002) The traveling salesman problem and its variations. Kluwer Academic Publishers, London
Held M, Karp RM (1970) The traveling salesman problem and minimum spanning trees. Oper Res 18:1138–1162
Hua Z, Huang F (2006) A variable-grouping based genetic algorithm for large-scale integer programming. Inf Sci 176(19):2869–2885
Karadogan A, Karci A (2013) Artificial atom algorithm for reinforcement learning. In: proceedings of 2nd international eurasian conference on mathematical sciences and applications, Sarajevo, Bosnia and Herzegovina, pp 379
Karci A, Arslan A (2002) Uniform population in genetic algorithms. I.U. J Electr Electron 2(2):495–504
Karci A, Alatas B, Akin E (2006) Sapling growing up algorithm (in Turkish). In: ASYU Akilli Sistemlerde Yenilikler ve Uygulamalari Sempozyomu. Istanbul, Turkey, pp 57–61
Karci A (2007) Theory of saplings growing-up algorithm. LNCS 4431:450–460
Karci A (2012) Anew meta-heuristic algorithm based on chemical process: Atom algorithm. In: proceedings of 1st international eurasian conference on mathematical sciences and applications. Prishtine, Kosova, pp 85–86
Knox J (1994) Tabu search performance on the symmetric traveling salesman problem. Comput Oper Res 21(8):867–876
Lawler EL, Lenstra JK, Shmoys DB (1985) The traveling salesman problem: a guided tour of combinatorial optimization. Series in discrete mathematics & optimization, Wiley, Chichester
Lin S (1965) Computer solutions of the traveling salesman problem. Bell Syst J 44:2245–2269
Lo CC, Hus CC (1998) Annealing framework with learning memory. IEEE Trans Syst Man Cybern Part A 28(5):1–13
Masutti TA, de Castro LN (2009) A self-organizing neural network using ideas from the immune system to solve the traveling salesman problem. Inf Sci 179(10):1454–1468
Miller CE, Tucker AW, Zemlin RA (1960) Integer programming formulation of traveling salesman problems. J ACM (JACM) 4:326–329
Mudaliar DN, Modi NK (2013) Unraveling travelling salesman problem by genetic algorithm using m-crossover operator. In: proceedings of international conference on signal processing. Image Processing and Pattern Recognition, Coimbatore, pp 127–130
Onwubolu GC, Clerc M (2004) Optimal path for automated drilling operations by a new heuristic approach using particle swarm optimization. Int J Prod Res 42(3):473–491
Ouaarab A, Ahiod B, Yang XS (2014) Discrete cuckoo search algorithm for the travelling salesman problem. Neural Comput Appl 24:1659–1669
Ozdag R, Karci A (2013) The application of electromagnetism-like algorithm for the dynamic deployment problem in wireless sensor networks. In: proceedings of 2nd international eurasian conference on mathematical sciences and applications. Sarajevo, Bosnia and Herzegovina, pp 199–200
Ozturk C, Karaboga D, Gorkemli B (2012) Artificial bee colony algorithm for dynamic deployment of wireless sensor networks. Turk J Elec Eng Comput Sci 20:255–262
Peker M, Sen B, Kumru PY (2013) An efficient solving of the traveling salesman problem: the ant colony system having parameters optimized by the Taguchi method. Turk J Elec Eng Comput Sci 21:2015–2036
Shang G (2005) The chaotic ant colony algorithm to solve TSP problem. Syst Eng Theory and Prac 9:100
Shen G, Zhang YQ (2011) A new evolutionary algorithm using shadow price guided operators. Appl Soft Comput 11(2):1983–1992
Wong LP, Low MYH, Chong CS (2008) A bee colony optimization algorithm for traveling salesman problem. In: proceedings of second Asia international conference on modelling and simulation (AMS). Kuala Lumpur, Malaysia, pp 818–823
Xu H, Qian X, Zhang L (2012) Study of ACO algorithm optimization based on improved tent chaotic mapping. J Inform Comput Sci 9(6):1653–1660
Yang XS (2009) Firefly algorithms for multimodal optimization. Stoch Algorithms: Found Appl SAGA, LNCS 5792:169–178
Yildirim AE, Karci A (2013) Solutions of travelling salesman problem using genetic algorithm and atom algorithm. In: proceedings of 2nd international eurasian conference on mathematical sciences and applications, Sarajevo, Bosnia and Herzegovina, pp 134
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Yildirim, A.E., Karci, A. Applications of artificial atom algorithm to small-scale traveling salesman problems. Soft Comput 22, 7619–7631 (2018). https://doi.org/10.1007/s00500-017-2735-z
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DOI: https://doi.org/10.1007/s00500-017-2735-z