  This paper introduces a new path planning algorithm for unmanned aerial
vehicles (UAVs) based on the teaching-learning-based optimization (TLBO)
technique. We first define an objective function that incorporates requirements
on the path length and constraints on the movement and safe operation of UAVs
to convert the path planning into an optimization problem. The optimization
algorithm named Multi-subject TLBO is then proposed to minimize the formulated
objective function. The algorithm is developed based on TLBO but enhanced with
new operations including mutation, elite selection and multi-subject training
to improve the solution quality and speed up the convergence rate. Comparison
with state-of-the-art algorithms and experiments with real UAVs have been
conducted to evaluate the performance of the proposed algorithm. The results
confirm its validity and effectiveness in generating optimal, collision-free
and flyable paths for UAVs in complex operating environments.
