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Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning

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  • Kai Yit Kok
  • Parvathy Rajendran

Abstract

The differential evolution algorithm has been widely applied on unmanned aerial vehicle (UAV) path planning. At present, four random tuning parameters exist for differential evolution algorithm, namely, population size, differential weight, crossover, and generation number. These tuning parameters are required, together with user setting on path and computational cost weightage. However, the optimum settings of these tuning parameters vary according to application. Instead of trial and error, this paper presents an optimization method of differential evolution algorithm for tuning the parameters of UAV path planning. The parameters that this research focuses on are population size, differential weight, crossover, and generation number. The developed algorithm enables the user to simply define the weightage desired between the path and computational cost to converge with the minimum generation required based on user requirement. In conclusion, the proposed optimization of tuning parameters in differential evolution algorithm for UAV path planning expedites and improves the final output path and computational cost.

Suggested Citation

  • Kai Yit Kok & Parvathy Rajendran, 2016. "Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-12, March.
    • Handle: RePEc:plo:pone00:0150558
    DOI: 10.1371/journal.pone.0150558
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    References listed on IDEAS

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    1. Sun, Fengchun & Hu, Xiaosong & Zou, Yuan & Li, Siguang, 2011. "Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles," Energy, Elsevier, vol. 36(5), pages 3531-3540.
    2. Lei Zhang & Zhenpo Wang & Fengchun Sun & David G. Dorrell, 2014. "Online Parameter Identification of Ultracapacitor Models Using the Extended Kalman Filter," Energies, MDPI, vol. 7(5), pages 1-14, May.
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    Cited by:

    1. Zengliang Han & Dongqing Wang & Feng Liu & Zhiyong Zhao, 2017. "Multi-AGV path planning with double-path constraints by using an improved genetic algorithm," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-16, July.
    2. ZhengQiang Xiong & Qiuze Yu & Tao Sun & Wen Chen & Yuhao Wu & Jie Yin, 2020. "Super-resolution reconstruction of real infrared images acquired with unmanned aerial vehicle," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-18, June.

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