A new vibrational genetic algorithm enhanced with a Voronoi diagram for path planning of autonomous UAV

A new optimization algorithm called multi-frequency vibrational genetic algorithm (mVGA) that can be used to solve the path planning problems of autonomous unmanned aerial vehicles (UAVs) is significantly improved. The algorithm emphasizes a new mutation application strategy and diversity variety such as the global random and the local random diversity. Clustering method and Voronoi diagram concepts are used within the initial population phase of mVGA process. The new algorithm and three additional GAs in the literature are applied to the path planning problem in two different three-dimensional (3D) environments such as sinusoidal and city type terrain models, and their results are compared. For both of the demonstration problems considered, remarkable reductions in the computational times have been accomplished.