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Journals / Turkish Journal of Electrical Engineering and Computer Science / 2013 / Cilt: 21 - Sayı: Sup.1

An efficient solving of the traveling salesman problem: the ant colony system having parameters optimized by the Taguchi method

Musa PEKER
Baha ŞEN
Pınar Yıldız KUMRU
Pages
2015–2036
Publication Date
2012-12-01
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Implementation software was developed using the MATLAB program, which has a useful interface and simulation support.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Owing to its complexity, the traveling salesman problem (TSP) is one of the most intensively studied problems in computational mathematics.
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Key Findings & Data:The implementation was tested by various data sets in the Traveling Salesman Problem Library and a performance analysis was undertaken.
💡
Conclusion & Impact:Implementation software was developed using the MATLAB program, which has a useful interface and simulation support.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Ant colony system · Taguchi method · route planning

Abstract

Owing to its complexity, the traveling salesman problem (TSP) is one of the most intensively studied problems in computational mathematics. The TSP is defined as the provision of minimization of total distance, cost, and duration by visiting the n number of points only once in order to arrive at the starting point. Various heuristic algorithms used in many fields have been developed to solve this problem. In this study, a solution was proposed for the TSP using the ant colony system and parameter optimization was taken from the Taguchi method. The implementation was tested by various data sets in the Traveling Salesman Problem Library and a performance analysis was undertaken. In addition to these, a variance analysis was undertaken in order to identify the effect values of the parameters on the system. Implementation software was developed using the MATLAB program, which has a useful interface and simulation support.

Özet

Owing to its complexity, the traveling salesman problem (TSP) is one of the most intensively studied problems in computational mathematics. The TSP is defined as the provision of minimization of total distance, cost, and duration by visiting the n number of points only once in order to arrive at the starting point. Various heuristic algorithms used in many fields have been developed to solve this problem. In this study, a solution was proposed for the TSP using the ant colony system and parameter optimization was taken from the Taguchi method. The implementation was tested by various data sets in the Traveling Salesman Problem Library and a performance analysis was undertaken. In addition to these, a variance analysis was undertaken in order to identify the effect values of the parameters on the system. Implementation software was developed using the MATLAB program, which has a useful interface and simulation support.

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Cite This Article

PEKER, M., ŞEN, B., KUMRU, P. Y. (2012). An efficient solving of the traveling salesman problem: the ant colony system having parameters optimized by the Taguchi method. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 21 - Sayı: Sup.1, 2015–2036. https://ajindex.com/articles/188594

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References (8)

  1. B. ¨ Ozkan, U. Cevre, A. U˘ gur, “[Route planning with a hybrid optimization technique]”, Akademik Bili¸sim, Vol. 44, pp. 173–183, 2008 (article in Turkish with an abstract in English). ˙I. Me¸secan, ˙I. ¨ O. Bucak, ¨ O. Asilkan, “Searching for the shortest path through group processing for TSP”, Mathematical and Computational Applications, Vol. 16, pp. 53–65, 2011. J.Y. Potvin, “The TSP: a neural network perspective”, ORSA Journal on Computing, Vol. 5, pp. 328–348, 1993.
  2. L.M. Gambardella, M. Dorigo, “Solving symmetric and asymmetric TSPs by ant colonies”, Proceedings of IEEE International Conference on Evolutionary Computation, pp. 622–627, 1996.
  3. S. Yigit, B. Tugrul, F.V Celebi, “A complete CAD model for type-I quantum cascade lasers with the use of artificial bee colony algorithm”, Journal of Artificial Intelligence, Vol. 5, p. 76, 2012. T. Keskint¨ urk, H, S¨ oyler, “Global ant colony optimization”, Journal of the Faculty of Engineering and Architecture of Gazi University, Vol. 21, pp. 689–698, 2006.
  4. M. Peker, B. S ¸en, S ¸. Bayır, “Using artificial intelligence techniques for large scale set partitioning problems”, World Conference on Innovation and Software Development, ˙Istanbul, 2011. C. Blum, “Ant colony optimization: Introduction and recent trends”, Physics of Life Reviews, Vol. 2, pp. 353–373, 200 TSPLIB. University of Heidelberg. Available at http://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/ (accessed 13.04.2011).
  5. B. Angeniol, G.S.L.C Vaubois, J.Y.L. Texier, “Self-organizing feature maps and the traveling salesman problem”, Neural Networks, Vol. 1, pp. 289–293, 1988.
  6. S. Somhom, A. Modares, T. Enkawa, “A self-organizing model for the traveling salesman problem”, Journal of the Operational Research Society, Vol. 48, pp. 919–928. 1997.
  7. K. Alaykıran, O. Engin, “Ant colony meta heuristic and an application on TSP”, Journal of the Faculty of Engineering and Architecture of Gazi University, Vol. 1, pp. 69–76, 2005.
  8. R. Pasti, L.N.D. Castro, “A neuro-immune network for solving the traveling salesman problem”, Proceedings of the International Joint Conference on Neural Networks, pp. 3760–3766, 2006. I. Vallivaara, “A team ant colony optimization algorithm for the multiple travelling salesmen problem with minmax objective”, 27th IASTED International Conference on Modelling, Identification, and Control (MIC 2008), pp. 1–6, 200 M. Dorigo, L.M. Gambardella, “Ant colony system: a cooperative learning approach to the traveling salesman problem”,