An Effective Guided Fireworks Algorithm for Solving UCAV Path Planning Problem

  • Adis AlihodzicEmail author
  • Damir Hasic
  • Elmedin Selmanovic
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11189)


The use of the unmanned aerial vehicles is rapidly growing in the ever more extensive range of applications where the military is the oldest ones. One of the fundamental problems in the unmanned combat aerial vehicles (UCAV) control is the path planning problem that refers to optimization of the flight route subject to various constraints inside the battlefield environments. Since the number of control points is high as well as the number of radars, the traditional methods could not produce acceptable results when tackling this problem. In this paper, we propose an adjustment of the recent guided fireworks algorithm from the class of swarm intelligence algorithms for locating the optimal path by unmanned combat aerial vehicle taking into consideration fuel consumption and safety degree. For experimental purposes, we compared it with eight different methods from the literature. Based on the experimental results, it can be concluded that our proposed approach is robust, exhibits better performance in almost all cases.


Unmanned combat aerial vehicle Path planning Swarm intelligence Metaheuristics Fireworks algorithm 


  1. 1.
    Duan, H.B., Zhang, X.Y., Wu, J., Ma, G.J.: Max-min adaptive ant colony optimization approach to multi-UAVs coordinated trajectory replanning in dynamic and uncertain environments. J. Bionic Eng. 6(2), 161–173 (2009)CrossRefGoogle Scholar
  2. 2.
    Unmanned Aerial Vehicle (UAV) Market by Application, Class, System, UAV Type, Mode of Operation, Range, Point of Sale, MTOW, and Region - Global Forecast to 2025 (2018).
  3. 3.
    Alihodzic, A.: Fireworks algorithm with new feasibility-rules in solving UAV path planning. In: The 2016 International Conference on Soft Computing and Machine Intelligence (ISCMI 2016), pp. 53–57, November 2016Google Scholar
  4. 4.
    Alihodzic, A., Tuba, E., Capor-Hrosik, R., Dolicanin, E., Tuba, M.: Unmanned aerial vehicle path planning problem by adjusted elephant herding optimization. In: Proceedings of the 25th Conference on Telecommunications Forum Telfor (TELFOR 2017), pp. 804–807 (2017)Google Scholar
  5. 5.
    Brajevic, I., Ignjatovic, J.: An upgraded firefly algorithm with feasibility-based rules for constrained engineering optimization problems. J. Intell. Manuf., 1–30 (2018)Google Scholar
  6. 6.
    Brintaki, A.N., Nikolos, I.K.: Coordinated UAV path planning using differential evolution. Oper. Res. 5(3), 487–502 (2005)Google Scholar
  7. 7.
    Dolicanin, E., Fetahovic, I., Tuba, E., Capor-Hrosik, R., Tuba, M.: Unmanned combat aerial vehicle path planning by brain storm optimization algorithm. Stud. Inform. Control 27(1), 15–24 (2018)CrossRefGoogle Scholar
  8. 8.
    Wang, G., Guo, L., Duan, H., Liu, L., Wang, H.: A modified firefly algorithm for UCAV path planning. Int. J. Hybrid Inf. Technol. 5, 123 (2012)Google Scholar
  9. 9.
    Kabamba, P.T., Meerkov, S.M., Zeitz, F.H.: Optimal UCAV path planning under missile threats. In: IFAC Proceedings Volumes, vol. 38, no. 1, pp. 289–294 (2005)CrossRefGoogle Scholar
  10. 10.
    Khatib, W., Fleming, P.J.: The stud GA: a mini revolution? In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 683–691. Springer, Heidelberg (1998). Scholar
  11. 11.
    Kurnaz, S., Cetin, O., Kaynak, O.: Adaptive neurofuzzy inference system based autonomous flight control of unmanned air vehicles. Expert Syst. Appl. 37(2), 1229–1234 (2010)CrossRefGoogle Scholar
  12. 12.
    Li, J., Zheng, S., Tan, Y.: The effect of information utilization: introducing a novel guiding spark in the fireworks algorithm. IEEE Trans. Evol. Comput. 21(1), 153–166 (2017)CrossRefGoogle Scholar
  13. 13.
    Li, S., Sun, X., Xu, Y.: Particle swarm optimization for route planning of unmanned aerial vehicles. In: 2006 IEEE International Conference on Information Acquisition, pp. 1213–1218 (2006)Google Scholar
  14. 14.
    Tan, Y., Zhu, Y.: Fireworks algorithm for optimization. In: Tan, Y., Shi, Y., Tan, K.C. (eds.) ICSI 2010. LNCS, vol. 6145, pp. 355–364. Springer, Heidelberg (2010). Scholar
  15. 15.
    Valavanis, K.P., Vachtsevanos, G.J.: Handbook of Unmanned Aerial Vehicles. Springer, Dordrecht (2015)CrossRefGoogle Scholar
  16. 16.
    Wang, G., Guo, L., Duan, H., Liu, L., Wang, H.: A bat algorithm with mutation for UCAV path planning. Sci. World J. 2012, 15 (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Adis Alihodzic
    • 1
    Email author
  • Damir Hasic
    • 1
  • Elmedin Selmanovic
    • 1
  1. 1.Department of MathematicsUniversity of SarajevoSarajevoBosnia and Herzegovina

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