Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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). https://www.researchandmarkets.com/research/thch75/global_unmanned?w=4
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 2016
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)
Brajevic, I., Ignjatovic, J.: An upgraded firefly algorithm with feasibility-based rules for constrained engineering optimization problems. J. Intell. Manuf., 1–30 (2018)
Brintaki, A.N., Nikolos, I.K.: Coordinated UAV path planning using differential evolution. Oper. Res. 5(3), 487–502 (2005)
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)
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)
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)
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). https://doi.org/10.1007/BFb0056910
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)
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)
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)
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). https://doi.org/10.1007/978-3-642-13495-1_44
Valavanis, K.P., Vachtsevanos, G.J.: Handbook of Unmanned Aerial Vehicles. Springer, Dordrecht (2015)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Alihodzic, A., Hasic, D., Selmanovic, E. (2019). An Effective Guided Fireworks Algorithm for Solving UCAV Path Planning Problem. In: Nikolov, G., Kolkovska, N., Georgiev, K. (eds) Numerical Methods and Applications. NMA 2018. Lecture Notes in Computer Science(), vol 11189. Springer, Cham. https://doi.org/10.1007/978-3-030-10692-8_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-10692-8_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-10691-1
Online ISBN: 978-3-030-10692-8
eBook Packages: Computer ScienceComputer Science (R0)