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Ant Colony Optimization Meta-heuristic for Solving Real Travelling Salesman Problem

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Emerging Research in Computing, Information, Communication and Applications

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Abstract

Ant colony optimisation is a population-based advanced approach for finding the solution of difficult problems with the help of a bioinspired approach from the behaviour of natural ants. The ant colony algorithm is a propelled optimisation method which is utilised to take care of combinatorial optimisation problems. The significant features of this algorithm are the utilisation of a mixture of preinformation and postinformation for organizing great solutions. The ant colony algorithm is used in this paper for solving the travelling salesman problem of the real set of data and getting the optimal results on graphs. This algorithm is an meta-heuristic algorithm in which we used the 2-opt local search method for tour construction and roulette wheel selection method for selection of nodes while constructing the route. The results show that this algorithm can efficiently find the optimal path of the hundred cities with minimum time and cost.

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Authors and Affiliations

  1. Department of CSE, CT Institute of Technology & Research, Jalandhar, 144002, India

    Sourabh Joshi &  Sarabjit Kaur

Authors
  1. Sourabh Joshi
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  2. Sarabjit Kaur
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Corresponding author

Correspondence to Sourabh Joshi .

Editor information

Editors and Affiliations

  1. Nitte Meenakshi Institute of Techno, Banglore, Karnataka, India

    N. R. Shetty

  2. Master of Comp Applications, Nitte Meenakshi Inst of Tech, Bangalore, Karnataka, India

    N Hamsavath Prasad

  3. Nitte Meenakshi Institute of Techno, Bangalore, Karnataka, India

    N. Nalini

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© 2016 Springer Science+Business Media Singapore

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Sourabh Joshi, Sarabjit Kaur (2016). Ant Colony Optimization Meta-heuristic for Solving Real Travelling Salesman Problem. In: Shetty, N., Prasad, N., Nalini, N. (eds) Emerging Research in Computing, Information, Communication and Applications . Springer, Singapore. https://doi.org/10.1007/978-981-10-0287-8_5

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  • DOI: https://doi.org/10.1007/978-981-10-0287-8_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0286-1

  • Online ISBN: 978-981-10-0287-8

  • eBook Packages: EngineeringEngineering (R0)

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