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Updated: Nov 7, 2025

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A Hybrid Differential Symbiotic Organisms Search Algorithm for UAV Path Planning.

Lisu Huo1, Jianghan Zhu1, Zhimeng Li1

  • 1College of Systems Engineering, National University of Defense Technology, Changsha 410073, China.

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|April 30, 2021
PubMed
Summary

A new hybrid algorithm improves unmanned aerial vehicle (UAV) path planning by combining differential evolution and symbiotic organism search. This method efficiently finds optimal flight paths in complex 3D environments.

Keywords:
differential evolutionevolutionary algorithmparticle swarm optimizationpath planningsymbiotic organism searchunmanned aerial vehicle

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Area of Science:

  • Robotics and Automation
  • Artificial Intelligence
  • Aerospace Engineering

Background:

  • Unmanned aerial vehicle (UAV) path planning is essential for mission success.
  • Complex 3D environments with obstacles present significant challenges for UAV navigation.
  • Existing algorithms often struggle with balancing efficiency and global search capabilities.

Purpose of the Study:

  • To propose a novel hybrid algorithm for efficient UAV path planning.
  • To enhance the global search ability and local search capability of existing algorithms.
  • To improve the robustness and efficiency of UAV path planning in complex environments.

Main Methods:

  • A hybrid differential symbiotic organisms search (HDSOS) algorithm was developed.
  • The algorithm integrates differential evolution (DE) mutation strategies with symbiotic organism search (SOS) modified strategies.
  • A traction function and perturbation strategy were introduced to enhance efficiency and robustness.

Main Results:

  • The HDSOS algorithm demonstrated superior performance in both 2D and 3D path planning scenarios.
  • Comparative studies showed the proposed algorithm outperformed particle swarm optimization (PSO), DE, and SOS.
  • The algorithm effectively balances local and global search capabilities for optimal path finding.

Conclusions:

  • The HDSOS algorithm offers a robust and efficient solution for UAV path planning.
  • This hybrid approach significantly improves navigation in complex, obstacle-rich environments.
  • The findings suggest a promising direction for advancing autonomous UAV operations.