Abstract
Multi-Objective UAV path planning problem is to find an optimal path, which can satisfy multiple objectives at the same time and optimize other performance indicators in the case of considering multiple conflicting objectives, constraints and trade-offs. In order to solve the challenge of considering multiple constraints and responding to environmental changes in real time, we propose a UAV path planning method based on multi-objective dwarf mongoose optimization algorithm. Considering the general search efficiency and insufficient global convergence of the original algorithms, nonlinear factorial augmented search strategies, chaotic mapping, and on-the-fly search strategies have been proposed to address the above problems and to enhance the algorithmic population diversity and local optimization capability. Experimental results show that, compared with other algorithms, the proposed method has better performance and robustness in multi-target UAV path planning, and can effectively find high-quality non-inferior solution sets, which provides an effective solution for UAV path planning.
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
Similar content being viewed by others
References
Agrawal, P., Abutarboush, H.F., Ganesh, T., Mohamed, A.W.: Metaheuristic algorithms on feature selection: a survey of one decade of research. IEEE Access 9(3), 26766–26791 (2021)
Hayat, S., Yanmaz, E., Bettstetter, C., Brown, T.X.: Multi-objective drone path planning for search and rescue with quality-of-service requirements. Auton. Robot. 44(7), 1183–1198 (2020)
Deb, K., Sindhya, K., Hakanen, J.: Multi-objective optimization. Decis. Sci. 27(5), 161–200 (2016)
Majeed, A., Hwang, S.O.: A multi-objective coverage path planning algorithm for UAVs to cover spatially distributed regions in urban environments. Aerospace 8(11), 343 (2021)
Zhen, X.U., Enze, Z., Qingwei, C.: Rotary unmanned aerial vehicles path planning in rough terrain based on multi-objective particle swarm optimization. J. Syst. Eng. Electron. 31(1), 130–141 (2020)
Wang, H., Tan, L., Shi, J., Lv, X., Lian, X.: An improved NSGA-II algorithm for UAV path planning problems. J. Internet Technol. 22(3), 583–592 (2021)
Tong, B., Chen, L., Duan, H.: A path planning method for UAVs based on multi-objective pigeon-inspired optimisation and differential evolution. Int. J. Bio-Inspired Comput. 17(2), 105–112 (2021)
Peng, C., Qiu, S.: A decomposition-based constrained multi-objective evolutionary algorithm with a local infeasibility utilization mechanism for UAV path planning. Appl. Soft Comput. 118, 108495 (2022)
Gupta, M., Varma, S.: Optimal placement of UAVs of an aerial grid network in an emergency situation. J. Ambient. Intell. Humaniz. Comput. 12, 343–358 (2021)
Agushaka, J.O., Ezugwu, A.E., Abualigah, L.: Dwarf mongoose optimization algorithm. Comput. Methods Appl. Mech. Eng. 391, 114570 (2022)
Gharehchopogh, F.S., Namazi, M., Ebrahimi, L., Abdollahzadeh, B.: Advances in sparrow search algorithm: a comprehensive survey. Arch. Comput. Methods Eng. 30(1), 427–455 (2023)
Kuo, R.J., Gosumolo, M., Zulvia, F.E.: Multi-objective particle swarm optimization algorithm using adaptive archive grid for numerical association rule mining. Neural Comput. Appl. 31(4), 3559–3572 (2019)
Dhiman, G., et al.: EMoSOA: a new evolutionary multi-objective seagull optimization algorithm for global optimization. Int. J. Mach. Learn. Cybern. 12, 571–596 (2021)
Dhiman, G., Kumar, V.: Emperor penguin optimizer: a bio-inspired algorithm for engineering problems. Knowl.-Based Syst. 159(7), 20–50 (2018)
Zhang, S., Liu, L.: A novel image encryption algorithm based on SPWLCM and DNA coding. Math. Comput. Simul. 190, 723–744 (2021)
Kaidi, W., Khishe, M., Mohammadi, M.: Dynamic levy flight chimp optimization. Knowl.-Based Syst. 235, 107625 (2022)
Ishibuchi, H., Nan, Y., Pang, L.M.: Performance evaluation of multi-objective evolutionary algorithms using artificial and real-world problems. In: Emmerich, M., et al. (eds.) EMO 2023. LNCS, vol. 13970, pp. 333–347. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-27250-9_24
Phung, M.D., Ha, Q.P.: Safety-enhanced UAV path planning with spherical vector-based particle swarm optimization. Appl. Soft Comput. 107, 107376 (2021)
Lin, J., Pan, L.: Multiobjective trajectory optimization with a cutting and padding encoding strategy for single-UAV-assisted mobile edge computing system. Swarm Evol. Comput. 75, 101163 (2022)
Acknowledgements
This research was supported by the Key Project of the Zhejiang Provincial Natural Science Foundation of China under Grant No. LZ24F020005.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, Q., Li, X., Su, P., Zhao, Y., Fu, Q. (2024). MODMOA: A Novel Multi-objective Optimization Algorithm for Unmanned Aerial Vehicle Path Planning. In: Pan, L., Wang, Y., Lin, J. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2023. Communications in Computer and Information Science, vol 2061. Springer, Singapore. https://doi.org/10.1007/978-981-97-2272-3_4
Download citation
DOI: https://doi.org/10.1007/978-981-97-2272-3_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-2271-6
Online ISBN: 978-981-97-2272-3
eBook Packages: Computer ScienceComputer Science (R0)