UAV Path Planning Based on Butterfly Optimization Algorithm in Three-Dimensional Space

The BOA is a novel optimization algorithm, which is inspired by the butterfly and enables the searching for the best solutions in a respective search area. The algorithm can be set to targeted goals like the amount of distance needed to cover, or/and the presence of an obstacle or the completion of the particular mission objectives. I applied the BOA to generate paths of UAVs on a three-dimensional space and considered the objectives of collision urgency, energy consumption, and near-optimal path planning. Specifically for the assessment of the algorithm, I simulated the application of MATLAB and apply multiple scenarios both on two-dimensional and on three-dimensional environments. I also benchmarked the BOA with two other algorithms including the Ant Colony Optimization and Particle Swarm Optimization (PSO). The results proved that the BOA performed better than the GA in terms of cost function and the time required to arrive at the optimal solution especially in 3D solid terrain. By analyzing the simulation results, the flexibility of the BOA in a 3D environment is evident when new changes take place in the environment. Moreover, the algorithm showed rather swift reaction in terms of path acting in response to various unexpected obstacles. The proposed BOA is viable for the path planning of UAVs in three-dimensional space and effective compared to the other optimization algorithms.