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Multi-strategy secretary bird optimization algorithm for UAV path planning in complex environment.

Le Feng^1,2,3, Huanxi Liu⁴, Zhifu He⁵

¹Huaneng Clean Energy Research Institute, Beijing, 100000, China.

Scientific Reports

|November 29, 2025

Summary

This summary is machine-generated.

This study introduces an improved Multi-strategy Secretary Bird Optimization Algorithm (MSBOA) for Unmanned Aerial Vehicle (UAV) path planning. The enhanced algorithm effectively navigates complex terrains, designing faster, shorter, and safer flight paths.

Keywords:

Dynamic fitness distance balance strategy Global exploration and local exploration Pooling mechanism Secretary bird optimization algorithm UAV path planning

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Area of Science:

Robotics
Artificial Intelligence
Optimization Algorithms

Background:

Unmanned Aerial Vehicle (UAV) path planning in complex terrains presents significant challenges.
Existing optimization algorithms may struggle with balancing exploration and exploitation, leading to suboptimal solutions or premature convergence.
The need for efficient and accurate path planning is crucial for various UAV applications.

Purpose of the Study:

To propose an improved Multi-strategy Secretary Bird Optimization Algorithm (MSBOA) for UAV path planning.
To enhance population diversity and optimization capabilities through a pooling mechanism.
To improve convergence accuracy and avoid local optima using dynamic fitness distance balance and reverse learning techniques.

Main Methods:

Developed a Multi-strategy Secretary Bird Optimization Algorithm (MSBOA) incorporating a pooling mechanism for population diversity.
Implemented a dynamic fitness distance balance technique to manage exploration-exploitation trade-offs.
Utilized a greedy selection-based centroid reverse learning approach for population updates.
Validated the MSBOA against benchmark functions (CEC2017) and a simulated UAV environment.

Main Results:

The proposed MSBOA demonstrated faster convergence and higher accuracy compared to the traditional Secretary Bird Optimization Algorithm (SBOA).
Simulation results confirmed the algorithm's effectiveness in solving complex UAV path planning problems.
The MSBOA successfully generated faster, shorter, and safer flight paths.

Conclusions:

The Multi-strategy Secretary Bird Optimization Algorithm (MSBOA) offers a robust and efficient solution for UAV path planning in complex environments.
The integrated strategies significantly enhance the algorithm's performance in terms of speed and accuracy.
MSBOA shows considerable potential for practical applications in autonomous navigation and aerial robotics.