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Police UAV path planning method based on improved PSO using AFS and HJS.

Dongxing Wang1,2, Xiaoxiao Qian3

  • 1Department of Computer and Information Security Management, Fujian Police College, Fuzhou, 350007, China. wangdongxing85@163.com.

Scientific Reports
|March 6, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an improved Particle Swarm Optimization (PSO) with Adaptive Factor Strategy (AFS) and Half Jumping Strategy (HJS) to enhance police unmanned aerial vehicle (UAV) path planning, achieving superior optimization accuracy and speed.

Keywords:
Adaptive factorHalf jumpingPSOPath planningPolice UAV

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Area of Science:

  • Robotics and Control Systems
  • Artificial Intelligence and Optimization Algorithms
  • Public Security and Surveillance Technology

Background:

  • Police unmanned aerial vehicles (UAVs) are crucial for enhancing public security operational capabilities.
  • Path planning is a significant challenge for police UAVs, impacting mission effectiveness.
  • Existing optimization algorithms have limitations in addressing complex police UAV path planning problems.

Purpose of the Study:

  • To develop an advanced optimization algorithm for police UAV path planning.
  • To improve the exploration and exploitation capabilities of Particle Swarm Optimization (PSO).
  • To achieve a globally optimal solution for police UAV path planning (PU3P).

Main Methods:

  • Proposed an improved PSO algorithm (AFS-HJS-PSO) integrating Adaptive Factor Strategy (AFS) and Half Jumping Strategy (HJS).
  • Utilized AFS-HJS-PSO to optimize the police UAV path planning fitness function (UPPF).
  • Conducted comparative experiments against PSO, Genetic Algorithm (GA), Simulated Annealing Algorithm (SAA), and FMINCON on benchmark functions and UPPF.

Main Results:

  • The AFS-HJS-PSO algorithm demonstrated superior optimization accuracy, convergence speed, and stability compared to PSO, GA, SAA, and FMINCON.
  • The AFS-HJS-PSO-PU3P method significantly outperformed other compared methods in solving the police UAV path planning problem.
  • Experimental results validated the effectiveness of the proposed AFS-HJS-PSO algorithm for complex optimization tasks.

Conclusions:

  • The AFS-HJS-PSO algorithm offers a robust and efficient solution for complex optimization problems, particularly in UAV path planning.
  • The proposed method enhances the performance of police UAVs in public security applications.
  • This research contributes to advancing intelligent optimization techniques for critical operational scenarios.