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Adaptive Dynamic Probabilistic Elitist Ant Colony Optimization in Traveling Salesman Problem

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Abstract

The ant colony optimization (ACO) is a population-based metaheuristic algorithm for the optimization problem, inspired by the foraging behavior of ants in the ant colony. One of its variants, the elitist ACO, further reinforces itself with the additional pheromone deposit to find the best path. Even though this usually leads to converging on the solution faster, it also has the drawback of getting stuck in local minima. In this paper, we describe a variation on elitist ACO where the pheromone contribution of the best path is further predicated by a probability factor. This probabilistic elitist ACO often produces a better solution for TSP, at the cost of a higher number of iterations. Some experimental results of this probabilistic elitist ACO are presented.

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References

  1. Parpinelli RS, Lopes HS, Freitas AA. Data mining with an ant colony optimization algorithm. IEEE Trans Evol Comput. 2002;6(4):321–32.

    Article  MATH  Google Scholar 

  2. Feller E, Rilling L, Morin C. Energy-aware ant colony based workload placement in clouds. In: Proceedings of the IEEE/ACM international conference on grid computing (GRID); 2011, p. 26–33.

  3. Dorigo M, Stützle T. The ant colony optimization metaheuristic: algorithms, applications, and advances. In: Glover F, Kochenberger GA, editors. Handbook of metaheuristics. Boston: Springer; 2003. p. 250–85.

    Chapter  Google Scholar 

  4. Zhang Z, Zhang X. A load balancing mechanism based on ant colony and complex network theory in open cloud computing federation. In: Proceedings of the 2nd international conference on industrial mechatronics and automation; 2010, p. 240–243.

  5. Blum C. Ant colony optimization: introduction and recent trends. Phys Life Rev. 2005;2(4):353–73.

    Article  Google Scholar 

  6. Hajizadeh Y, Christie M, Demyanov V. Ant colony optimization for history matching and uncertainty quantification of reservoir models. J Pet Sci Eng. 2011;77(1):78–92.

    Article  Google Scholar 

  7. Dorigo M, Di Caro G, Gambardella LM. Ant algorithms for discrete optimization. Artif Life. 1999;5(2):137–72.

    Article  Google Scholar 

  8. Colorni A, Dorigo M, Maniezzo V. Distributed optimization by ant colonies. In: The European conference on artificial life; 1991, p. 134–142.

  9. Dorigo M, Gambardella LM. Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput. 1997;1:53–6.

    Article  Google Scholar 

  10. Maier HR, et al. Ant colony optimization for design of water distribution systems. J Water Resour Plan Manag. 2003;129(3):200–9.

    Article  Google Scholar 

  11. Di Caro G, Dorigo M. Ant colony optimization and its application to adaptive routing in telecommunication networks; 2004.

  12. Brezina I Jr, Čičková Z. Solving the travelling salesman problem using the ant colony optimization. Manag Inf Syst. 2011;16(4):010–4.

    Google Scholar 

  13. Dorigo M. Learning and natural algorithms. Ph.D. thesis, Politecnico di Milano, Milan, Italy, 1992.

  14. Cordón O, de Viana IF, Herrera F. Analysis of the best-worst ant system and its variants on the TSP. Mathw Soft Comput. 2002;9(2/3):177–92.

    MathSciNet  MATH  Google Scholar 

  15. Bullnheimer B, Hartl RF, Strauss C. A new rank based version of the ant system. A computational study. Cent Eur J Oper Res Econ. 1997;7:25–38.

    MathSciNet  MATH  Google Scholar 

  16. Li B, Wang L, Song W. Ant colony optimization for the traveling salesman problem based on ants with memory. In: ICNC 2008; 2008, p. 496–501.

  17. Lee Z, Lee CY, Su SF. An immunity-based ant colony optimization algorithm for solving weapon–target assignment problem. Appl Soft Comput. 2002;2(1):39–47.

    Article  Google Scholar 

  18. Durbin R, Willshaw D. An analogue approach to the travelling salesman problem using an elastic net method. Nature. 1987;326:689–91.

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND, 58202-9015, USA

    Amrita Chatterjee, Eunjin Kim & Hassan Reza

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  1. Amrita Chatterjee
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  2. Eunjin Kim
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  3. Hassan Reza
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Correspondence to Amrita Chatterjee.

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This article is part of the topical collection “Advances in Computational Intelligence, Paradigms and Applications” guest edited by Young Lee and S. Meenakshi Sundaram.

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Chatterjee, A., Kim, E. & Reza, H. Adaptive Dynamic Probabilistic Elitist Ant Colony Optimization in Traveling Salesman Problem. SN COMPUT. SCI. 1, 95 (2020). https://doi.org/10.1007/s42979-020-0083-z

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  • DOI: https://doi.org/10.1007/s42979-020-0083-z

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