Finding the shortest path using the ant colony optimization

  • Authors

    • K Yella Swamy
    • Saranya Gogineni
    • Yaswanth Gunturu
    • Deepchand Gudapati
    • Ramu Tirumalasetti
    2017-12-21
    https://doi.org/10.14419/ijet.v7i1.1.9859
  • Optimization, Quadratic, Pheromone, Trails, NP-Hard.

  • An ant colony optimization(ACO) is a techniquewhich is recently introduced ,and it is applied to solve several np-hard problems ,we can get optimal solution in a short time Main concept of the ACO is based on the behavior of ants in their colony for finding a source of food. They will communicate indirectly through pheromone trails. Computer based simulation is can generate good solution by using artificial ants, by using that general behavior we are solving travelling Sale man problem.

  • References

    1. [1] Dweepna Garg and Saurabh Shah “Ant colony optimization for solving traveling salesman problem†vol 5,no 1,January-june 2011

      [2] Potyin J.Y (1993), The Traveling salesman problem: A Neural Network Perspective ORSA Journal of computing, 5 (4), 328-347.https://doi.org/10.1287/ijoc.5.4.328.

      [3] “Ant colony System. A cooperative learning approach to the traveling salesman problem,â€IEE transaction on Evolutionary computatiom, vol 1, no-1, pp 53-66, 1997.

      [4] M. Dorigo and G. Di caro,â€The Ant colony Optimization meta-heuristicâ€, in new in optimization, D.corneerl. eds., Mcgraw Hill, London, UK, pp. 11-32, 1999.

      [5] A.I &Kennington, J. L (1986).the asymmetric m-traveling salesman problem, a duality based branch and bound algorithm, Discrete Applied Mathematics,vol NO,13,pp-259-76.

      [6] Lin, S. (1986)â€computer solution of the traveling salesman problemâ€, Bell syst journal 44, 2245-2269.https://doi.org/10.1002/j.1538-7305.1965.tb04146.x.

      [7] Dorigo, M & Gambardella, (1997)â€Ant colonies for the traveling salesman problemâ€Biosystemsm43, 73-81.https://doi.org/10.1016/S0303-2647(97)01708-5.

      [8] “ACO algorithm with guaranteed convergence to the optimal solutionâ€, Information Processing Letters, vol, 82, no.3, pp 145-153, 2002.https://doi.org/10.1016/S0020-0190(01)00258-7.

      [9] Gao Shang Zhang Lei Zhuang fengting Zhang Chunxian School of electronics and information Jianguru University of science and technology, Zhenjiang 212003, china.

      [10] C.S Jeongnad M.H Kim, “Fast parallel simulated annealing for traveling salesman problemâ€, Neural Network, vol.3 (1990), pp.947-953.

      [11] JinhuiYang,Xiaohushi,MaurizioMarchese and sanctum Liang Yanchum Liang, â€Ant colony optimization for generalized TSP problemâ€, college of computer science Jilin university,changchum 130012,china.

      [12] Zalilah Abd Aziz, â€Ant colony Hyper-heuristics for Travelling Salesman Problemâ€, Mara University,2015 IEEE{IRIS 2015}

      [13] Michalis Mavrobouniotis Felipe M.MullerShengxiang yang, “Ant colony optimization with local search for Dynamic Travelling salesman problem: IEEE, vol.47 no. 7, july 2017.

      [14] Hassan Ismkhan,â€Effective heuristics for ant colony optimization to handle large scale problemâ€, swarm and evolutionary computation 32(2017)140-149

      [15] Srikantaparnaik,â€Route optimization by Ant colony optimization techniqueâ€, school of study on computer science & IT, Raipur(Chhattisgarh)492010,India

      [16] Mateuzsekara Michal Kowalski,AleksandarByrski and bipinindurkhya,â€Multi-pheromone ant colony optimization for socio-cognitive simulation processâ€.vol.51,2015.

      [17] Chi-bin cheng, chun-pin mao,†A modified ant colony system for solving he traveling salesman problemsâ€, mathematical and computer modelling 46(2007).

      [18] Viktor danchuk, oelnabakulich, vitaliysyatko,â€An improvement in ant algorithm method for optimizing a transport route with regard to raffix flowâ€, National transport university, Ukraine.

      [19] Aleksandar kaplarmilanvidakovic,nikolaluburic and mirjanaivanovic,â€improving a distributed agent based ant colony optimization for solving traveling salesman problemâ€, university of novi sad, Serbia.

      [20] LIU shufenLenf Huang and hanlu,â€Pheromone model selection in ant colony optimization for the traveling salesman problemâ€,jilin university, vol 26,No 2,13002,china

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  • How to Cite

    Yella Swamy, K., Gogineni, S., Gunturu, Y., Gudapati, D., & Tirumalasetti, R. (2017). Finding the shortest path using the ant colony optimization. International Journal of Engineering & Technology, 7(1.1), 392-396. https://doi.org/10.14419/ijet.v7i1.1.9859