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<p>In the development of the low-altitude economic field, unmanned aerial vehicle (UAV) three-dimensional (3D) path planning often suffers from slow convergence and local optima problems. To address these problems, this study established a comprehensive technical framework for path planning that spanned application scenario design to algorithm performance enhancement. A parametric stochastic 3D environment model was first established to simulate real-world uncertainties. Then, a multi-dimensional performance evaluation model integrating weather conditions and terrain types was developed. This model incorporated multiple core flight indicators, including the path length, safety, flight altitude, no-fly zone constraints, and path smoothness. Moreover, the weights of these indicators were dynamically adjusted to make the path planning results more aligned with actual operational needs. Based on this, an adaptive multi-strategy cooperative elk herd optimization (AMCEHO) algorithm with better chaotic ergodicity and optimization balance was proposed. Through the synergy of hybrid chaotic initialization, a dynamic elite pool, and multi-strategy collaborative updating, it not only overcame the population diversity limitation of a single chaotic map and calibrated search directions, but also balanced exploration and exploitation. The hierarchical correlation among the three components significantly enhanced the algorithm’s rapid convergence and global optimization capabilities, solving the key difficulties of traditional algorithms. Experiments were conducted for six benchmark functions and two scenarios (medium and complex obstacles scenarios), comparing the AMCEHO algorithm with several state-of-the-art algorithms. The results demonstrated that among the six benchmark functions, the AMCEHO algorithm achieved optimal values in the 10<sup>−2</sup> to 10<sup>−5</sup> range with minimal standard deviation, significantly enhancing the global optimization capabilities. Compared with other algorithms, the AMCEHO algorithm achieved up to 71.9% fewer iterations and 56.4% lower total path cost, while achieving a maximum 36.7% reduction in overall fitness. This study provides a promising solution for high-quality UAV 3D path planning in low-altitude economic field.</p>

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An adaptive multi-strategy cooperative elk herd optimization algorithm for three-dimensional unmanned aerial vehicle path planning

  • 期刊论文

  • 发表日期：

    2026年4月20日

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  • Xinchun Qiang,
  • Yuhong Gao,
  • Jiqian Yang,
  • Sinan Wang,
  • Zhenyu Yun,
  • Yingji Xia

摘要

In the development of the low-altitude economic field, unmanned aerial vehicle (UAV) three-dimensional (3D) path planning often suffers from slow convergence and local optima problems. To address these problems, this study established a comprehensive technical framework for path planning that spanned application scenario design to algorithm performance enhancement. A parametric stochastic 3D environment model was first established to simulate real-world uncertainties. Then, a multi-dimensional performance evaluation model integrating weather conditions and terrain types was developed. This model incorporated multiple core flight indicators, including the path length, safety, flight altitude, no-fly zone constraints, and path smoothness. Moreover, the weights of these indicators were dynamically adjusted to make the path planning results more aligned with actual operational needs. Based on this, an adaptive multi-strategy cooperative elk herd optimization (AMCEHO) algorithm with better chaotic ergodicity and optimization balance was proposed. Through the synergy of hybrid chaotic initialization, a dynamic elite pool, and multi-strategy collaborative updating, it not only overcame the population diversity limitation of a single chaotic map and calibrated search directions, but also balanced exploration and exploitation. The hierarchical correlation among the three components significantly enhanced the algorithm’s rapid convergence and global optimization capabilities, solving the key difficulties of traditional algorithms. Experiments were conducted for six benchmark functions and two scenarios (medium and complex obstacles scenarios), comparing the AMCEHO algorithm with several state-of-the-art algorithms. The results demonstrated that among the six benchmark functions, the AMCEHO algorithm achieved optimal values in the 10−2 to 10−5 range with minimal standard deviation, significantly enhancing the global optimization capabilities. Compared with other algorithms, the AMCEHO algorithm achieved up to 71.9% fewer iterations and 56.4% lower total path cost, while achieving a maximum 36.7% reduction in overall fitness. This study provides a promising solution for high-quality UAV 3D path planning in low-altitude economic field.