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Enhanced Student Psychology-Based Optimization (ESPBO) improves UAV trajectory planning by addressing local optima and enhancing convergence. This novel algorithm offers superior path accuracy and stability in complex environments.

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Area of Science:

  • Artificial Intelligence
  • Robotics
  • Optimization Algorithms

Background:

  • Standard Student Psychology-Based Optimization (SPBO) suffers from limited exploration, local optima, and poor convergence accuracy.
  • Three-dimensional Unmanned Aerial Vehicle (UAV) trajectory planning presents challenges in complex terrains and no-fly zones.

Purpose of the Study:

  • To enhance the SPBO algorithm for improved exploration, exploitation, and convergence accuracy in 3D UAV trajectory planning.
  • To introduce an Enhanced SPBO (ESPBO) algorithm with novel strategies for robust optimization.

Main Methods:

  • ESPBO integrates Time-Adaptive Scheduling, Mentor Pool Guidance, and Directional Jump Exploration.
  • Performance is evaluated against GWO, WOA, IAGWO, DBO, SO, RIME, and original SPBO on CEC benchmark functions.
  • Statistical stability is assessed using Wilcoxon rank-sum and Friedman tests.

Main Results:

  • ESPBO significantly outperforms comparison algorithms in path-planning accuracy and convergence stability, achieving the first rank.
  • In 3D UAV trajectory planning, ESPBO achieved an optimal path length of 199.8874 m and an average of 205.8179 m.
  • ESPBO's average path length was lower than the optimal path length of other tested algorithms.

Conclusions:

  • ESPBO offers an efficient and robust solution for UAV trajectory optimization in complex environments.
  • The enhanced algorithm demonstrates significant improvements over existing swarm intelligence methods.
  • ESPBO extends the applicability of swarm intelligence in autonomous navigation systems.