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Research on UAV Path Planning Based on an Improved Dwarf Mongoose Algorithm with Multi-strategy Fusion

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Advanced Intelligent Computing Technology and Applications (ICIC 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14862))

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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.

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References

  1. 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)

    Article  Google Scholar 

  2. 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)

    Article  Google Scholar 

  3. 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)

    Google Scholar 

  4. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey Wolf Optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  5. Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)

    Article  Google Scholar 

  6. Hashim, F.A., Hussien, A.G.: Snake optimizer: a novel meta-heuristic optimization algorithm. Knowl.-Based Syst. 242, 108320 (2022)

    Google Scholar 

  7. Xue, J.K., Shen, B.: Dung beetle optimizer: a new meta-heuristic algorithm for global optimization. J. Supercomput. 79, 7305–7336 (2023)

    Article  Google Scholar 

  8. Chai, Y., Zhu, Y., Ren, S.: An improved whale optimization algorithm based on multi-strategy coordination. Comput. Eng. Sci. 45, 1308–1319 (2023)

    Google Scholar 

  9. Agushaka, J.O., Ezugwu, A.E., Abualigah, L.: Dwarf mongoose optimization algorithm. Comput. Methods Appl. Mech. Eng. 391, 114570 (2022)

    Article  MathSciNet  Google Scholar 

  10. 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)

    Article  Google Scholar 

  11. 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)

    Google Scholar 

  12. 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)

    Google Scholar 

  13. Wang, X.Y., Jin, C.Q.: Image encryption using Game of Life permutation and PWLCM chaotic system. Opt. Commun. 285, 412–417 (2012)

    Article  Google Scholar 

  14. Yan, Y., Ma, H.Z., Li, Z.D.: An improved grasshopper optimization algorithm for global optimization. Chin. J. Electron. 30, 451–459 (2021)

    Article  Google Scholar 

  15. 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)

    Article  Google Scholar 

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Correspondence to Yu Zhang .

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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

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  • DOI: https://doi.org/10.1007/978-981-97-5578-3_28

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-5577-6

  • Online ISBN: 978-981-97-5578-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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