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Updated: Aug 27, 2025

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A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative

Yangyang Liu1, Pengyang Zhang1, Yu Ru2

  • 1School of Engineering, Anhui Agricultural University, Hefei, China.

Frontiers in Plant Science
|October 3, 2022
PubMed
Summary

This study introduces a novel algorithm for unmanned aerial vehicle (UAV) plant protection route planning in complex hilly terrains. The dynamic genetic algorithm with ant colony binary iterative optimization (DGA-ACBIO) significantly reduces flight range and search time for multi-tea field operations.

Keywords:
bionic algorithmhilly mountainous areamulti-tea field plant protectionscheduling route planningunmanned aerial vehicle

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Area of Science:

  • Agricultural Engineering
  • Robotics
  • Operations Research

Background:

  • Hilly and mountainous regions present significant challenges for effective plant protection operations due to complex terrain and weak infrastructure.
  • Existing route planning methods are often inefficient for large-scale agricultural tasks like tea cultivation in sloped areas.

Purpose of the Study:

  • To develop an optimized unmanned aerial vehicle (UAV) route planning algorithm for multi-tea field plant protection in complex hilly environments.
  • To enhance the efficiency and reduce the operational costs of agricultural spraying in challenging terrains.

Main Methods:

  • Development of a dynamic genetic algorithm (DGA) with adaptive crossover and mutation probabilities.
  • Proposal of an ant colony binary iteration optimization (ACBIO) incorporating iteration period and reinforcement concepts.
  • Serial fusion of DGA and ACBIO (DGA-ACBIO) for multi-regional route planning.

Main Results:

  • The DGA-ACBIO algorithm demonstrated a significant reduction in optimal flight range compared to existing algorithms (e.g., GA, ACO, AFSA, PSO).
  • Search time was reduced by over 50% compared to other bionic algorithms.
  • The algorithm showed superior performance, stability, planning accuracy, and search speed for complex route planning problems.

Conclusions:

  • The DGA-ACBIO algorithm effectively addresses the challenges of plant protection route planning in complex hilly tea fields.
  • This approach leads to reduced inter-regional scheduling distances and lower overall plant protection costs.
  • The developed algorithm offers a robust solution for optimizing UAV operations in challenging agricultural landscapes.