Multi-UAV Path Planning with Multi Colony Ant Optimization

  • Ugur Cekmez
  • Mustafa Ozsiginan
  • Ozgur Koray Sahingoz
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 736)


In the last few decades, Unmanned Aerial Vehicles (UAVs) have been widely used in different type of domains such as search and rescue missions, firefighting, farming, etc. To increase the efficiency and decrease the mission completion time, in most of these areas swarm UAVs, which consist of a team of UAVs, are preferred instead of using a single large UAV due to the decreasing the total cost and increasing the reliability of the whole system. One of the important research topics for the UAVs autonomous control system is the optimization of flight path planning, especially in complex environments. Lots of researchers get help from the evolutionary algorithms and/or swarm algorithms. However, due to the increased complexity of the problem with more control points which need to be checked and mission requirements, some additional mechanisms such as parallel programming and/or multi-core computing is needed to decrease the calculation time. In this paper, to solve the path planning problem of multi-UAVs, an enhanced version of Ant Colony Optimization (ACO) algorithm, named as multi-colony ant optimization, is proposed. To increase the speed of computing, the proposed algorithm is implemented on a parallel computing platform: CUDA. The experimental results show the efficiency and the effectiveness of the proposed approach under different scenarios.


Multi-UAV Multi Colony Ant Optimization GPGPU CUDA Parallel evolutionary computing 


  1. 1.
    Cekmez, U., Ozsiginan, M., Sahingoz, O.K.: Multi-UAV path planning with parallel genetic algorithms on CUDA architecture. In: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, pp. 1079–1086. ACM (2016)Google Scholar
  2. 2.
    Cekmez, U., Ozsiginan, M., Sahingoz, O.K.: Multi colony ant optimization for UAV path planning with obstacle avoidance. In: 2016 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 47–52. IEEE (2016)Google Scholar
  3. 3.
    Yueshun, H., Ping, D.: A study of a new multi-ant colony optimization algorithm. In: Advances in Information Technology and Industry Applications, pp. 155–161 (2012)Google Scholar
  4. 4.
    Middendorf, M., Reischle, F., Schmeck, H.: Multi colony ant algorithms. J. Heuristics 8(3), 305–320 (2002)CrossRefzbMATHGoogle Scholar
  5. 5.
    Blum, C., Dorigo, M.: Search bias in ant colony optimization: on the role of competition-balanced systems. IEEE Trans. Evol. Comput. 9(2), 159–174 (2005)CrossRefGoogle Scholar
  6. 6.
    Sim, K.M., Sun, W.H.: Multiple ant colony optimization for load balancing. In: International Conference on Intelligent Data Engineering and Automated Learning, pp. 467–471. Springer, Heidelberg (2003)Google Scholar
  7. 7.
    Neumann, F., Witt, C.: Bioinspired computation in combinatorial optimization: algorithms and their computational complexity. In: Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 567–590. ACM (2013)Google Scholar
  8. 8.
    DeleVacq, A., Delisle, P., Gravel, M., Krajecki, M.: Parallel ant colony optimization on graphics processing units. J. Parallel Distrib. Comput. 73(1), 52–61 (2013)CrossRefGoogle Scholar
  9. 9.
    Cekmez, U., Ozsiginan, M., Sahingoz, O.K.: A UAV path planning with parallel ACO algorithm on CUDA platform. In: 2014 International Conference in Unmanned Aircraft Systems (ICUAS), pp. 347–354. IEEE (2014)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Ugur Cekmez
    • 1
    • 2
  • Mustafa Ozsiginan
    • 2
  • Ozgur Koray Sahingoz
    • 3
  1. 1.Information Technologies InstituteTUBITAK BILGEMKocaeliTurkey
  2. 2.Institute of Pure and Applied SciencesMarmara UniversityIstanbulTurkey
  3. 3.Computer Engineering DepartmentIstanbul Kultur UniversityIstanbulTurkey

Personalised recommendations