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Lens Learning Sparrow Search Algorithm

Author

Listed:
  • Chengtian Ouyang
  • Donglin Zhu
  • Yaxian Qiu

Abstract

In this paper, a lens learning sparrow search algorithm (LLSSA) is proposed to improve the defects of the new sparrow search algorithm, which is random and easy to fall into local optimum. The algorithm has achieved good results in function optimization and has planned a safer and less costly path to the three-dimensional UAV path planning. In the discoverer stage, the algorithm introduces the reverse learning strategy based on the lens principle to improve the search range of sparrow individuals and then proposes a variable spiral search strategy to make the follower's search more detailed and flexible. Finally, it combines the simulated annealing algorithm to judge and obtain the optimal solution. Through 15 standard test functions, it is verified that the improved algorithm has strong search ability and mining ability. At the same time, the improved algorithm is applied to the path planning of 3D complex terrain, and a clear, simple, and safe route is found, which verifies the effectiveness and practicability of the improved algorithm.

Suggested Citation

  • Chengtian Ouyang & Donglin Zhu & Yaxian Qiu, 2021. "Lens Learning Sparrow Search Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-17, May.
    • Handle: RePEc:hin:jnlmpe:9935090
    DOI: 10.1155/2021/9935090
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    Cited by:

    1. Feng, Juqiang & Cai, Feng & Zhao, Yang & Zhang, Xing & Zhan, Xinju & Wang, Shunli, 2024. "A novel feature optimization and ensemble learning method for state-of-health prediction of mining lithium-ion batteries," Energy, Elsevier, vol. 299(C).
    2. Ke Yao & Yujie Chen & Yucheng Li & Xuesheng Zhang & Beibei Zhu & Zihao Gao & Fei Lin & Yimin Hu, 2024. "Water Quality Prediction of Small-Micro Water Body Based on the Intelligent-Algorithm-Optimized Support Vector Machine Regression Method and Unmanned Aerial Vehicles Multispectral Data," Sustainability, MDPI, vol. 16(2), pages 1-19, January.
    3. Mengmeng Qiao & Zexu Yu & Zhenhai Dou & Yuanyuan Wang & Ye Zhao & Ruishuo Xie & Lianxin Liu, 2022. "Study on Economic Dispatch of the Combined Cooling Heating and Power Microgrid Based on Improved Sparrow Search Algorithm," Energies, MDPI, vol. 15(14), pages 1-31, July.
    4. Guoyuan Ma & Xiaofeng Yue & Juan Zhu & Zeyuan Liu & Shibo Lu, 2023. "Deep Learning Network Based on Improved Sparrow Search Algorithm Optimization for Rolling Bearing Fault Diagnosis," Mathematics, MDPI, vol. 11(22), pages 1-20, November.
    5. Yifu Chen & Jun Li & Lin Zhang, 2023. "Learning Sparrow Algorithm With Non-Uniform Search for Global Optimization," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 14(1), pages 1-31, January.
    6. Yi Liang & Yingying Fan & Yongfang Peng & Haigang An, 2022. "Smart Grid Project Benefit Evaluation Based on a Hybrid Intelligent Model," Sustainability, MDPI, vol. 14(17), pages 1-20, September.
    7. Jian Chen & Jiajun Zhu & Xu Qin & Wenxiang Xie, 2023. "Reducing Octane Number Loss in Gasoline Refining Process by Using the Improved Sparrow Search Algorithm," Sustainability, MDPI, vol. 15(8), pages 1-21, April.

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