A diversified group teaching optimization algorithm with segment-based fitness strategy for unmanned aerial vehicle route planning

The complexity and diversity of the flight environment pose great challenges to unmanned aerial vehicle route planning, which demands feasible flight strategies and efficient route planning algorithms. To address the issue, this paper constructs a 3-D flight environment model with multiple obstacles, and designs a novel diversified group teaching optimization algorithm for the generation of flight routes of unmanned aerial vehicles. In the environment model, a variety of obstacles are taken into consideration to make the flying scenarios more realistic, including mountain, cuboid, cylinder and triangular prism, and corresponding strategies are presented for unmanned aerial vehicles to safely avoid these obstacles. In the proposed algorithm, three novel teaching methods are introduced to balance the exploitation and exploration phases. Besides, a novel constrained optimization strategy is adopted, in which constraints are incrementally added to the fitness function to avoid the premature phenomenon in the initial iteration stage of algorithm. The experimental results show that compared with several state-of-the-art optimization algorithms, the proposed algorithm is significantly superior and can always generate the optimal flight route in complicated environments.

Automated summary

This study addresses the challenges posed by the complexity and diversity of the flight environment for unmanned aerial vehicle route planning. It constructs a 3-D flight environment model with multiple obstacles and designs a novel diversified group teaching optimization algorithm for generating flight routes. The proposed algorithm shows significant superiority compared to state-of-the-art optimization algorithms and consistently generates optimal flight routes in complex environments.