Abstract
To address the issues of the Dwarf Mongoose Optimization Algorithm (DMOA) being prone to local optima and exhibiting low convergence accuracy during its operation, this paper introduces the Chaotic Dwarf Mongoose Optimization Algorithm (CDMOA). The CDMO algorithm employs a chaos mapping strategy to ensure a uniform distribution of the initial population across the solution space, thereby enhancing population diversity. Additionally, it utilizes an inverse learning strategy to bolster the global search capabilities of the algorithm. Comparative experiments conducted using benchmark test functions demonstrate that CDMOA outperforms the original DMO algorithm in terms of optimization performance, convergence accuracy, and algorithm stability. Finally, the application of CDMOA to drone flight path planning is presented. The simulation results indicate that the optimized flight paths generated by the improved algorithm are superior and more stable.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abd Elaziz, M., Lu, S., He, S.: A multi-leader whale optimization algorithm for global optimization and image segmentation. Expert Syst. Appl. 175, 114841 (2021)
Zhang, L., Zhang, Y.J., Li, Y.F.: Mobile robot path planning based on improved localized particle swarm optimization. IEEE Sens. J. 21, 6962–6972 (2021)
Yu, L.I., Ling, L.I.U., Zhu, X.U.E.: Parameter optimization of soil water characteristic curve model based on swarm intelligence algorithm. Water Saving Irrigation 57–65 (2023)
Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey Wolf Optimizer. Adv. Eng. Softw. 69, 46–61 (2014)
Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)
Hashim, F.A., Hussien, A.G.: Snake optimizer: a novel meta-heuristic optimization algorithm. Knowl.-Based Syst. 242, 108320 (2022)
Xue, J.K., Shen, B.: Dung beetle optimizer: a new meta-heuristic algorithm for global optimization. J. Supercomput. 79, 7305–7336 (2023)
Chai, Y., Zhu, Y., Ren, S.: An improved whale optimization algorithm based on multi-strategy coordination. Comput. Eng. Sci. 45, 1308–1319 (2023)
Agushaka, J.O., Ezugwu, A.E., Abualigah, L.: Dwarf mongoose optimization algorithm. Comput. Methods Appl. Mech. Eng. 391, 114570 (2022)
Agushaka, J.O., Ezugwu, A.E., Olaide, O.N., Akinola, O., Abu Zitar, R., Abualigah, L.: Improved dwarf mongoose optimization for constrained engineering design problems. J. Bionic Eng. 20, 1263–1295 (2023)
Jia, H., Chen, L., Li, S., Liu, Q., Wu, D., Lu, C.: Optimization algorithm of elite pool dwarf mongoose based on lens imaging reverse learning. Comput. Eng. Appl. 59, 131–139 (2023)
Ming-yang, Y.U., Ting, L.I., Jing, X.U.: Enhanced dwarf mongoose optimization algorithm with multi-strategy fusion. J. Beijing Univ. Aeronaut. Astronaut. (2024)
Wang, X.Y., Jin, C.Q.: Image encryption using Game of Life permutation and PWLCM chaotic system. Opt. Commun. 285, 412–417 (2012)
Yan, Y., Ma, H.Z., Li, Z.D.: An improved grasshopper optimization algorithm for global optimization. Chin. J. Electron. 30, 451–459 (2021)
Long, W., Jiao, J.J., Liang, X.M., Cai, S.H., Xu, M.: A random opposition-based learning grey wolf optimizer. IEEE ACCESS 7, 113810–113825 (2019)
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, H., Zhang, Y., Xu, S., Wang, F., Chen, B. (2024). Research on UAV Path Planning Based on an Improved Dwarf Mongoose Algorithm with Multi-strategy Fusion. In: Huang, DS., Zhang, X., Chen, W. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14862. Springer, Singapore. https://doi.org/10.1007/978-981-97-5578-3_28
Download citation
DOI: https://doi.org/10.1007/978-981-97-5578-3_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-5577-6
Online ISBN: 978-981-97-5578-3
eBook Packages: Computer ScienceComputer Science (R0)