UAV path planning based on butterfly optimization algorithm in three-dimensional space

Much studied have addressed Path planning as one of the main topics in unmanned aerial vehicles; which has yielded different results due to the existing conditions and limitations. To this end, the present study proposed an efficient algorithm underpinned by the propeller optimization algorithm, which had a utility function and could optimize multiple responses simultaneously. BOA is different from other meta-heuristic algorithms as each propeller produces its own unique fit in the path by combining information extracted from different sensory receptors. Accordingly, BOA can solve multi-objective problems. A 3D objective function was used in this study to estimate the length of the shortest path and the intensity of collisions with obstacles, avoid collisions and enhance UAVs’ operational capacity as a function of consumed energy. Furthermore, A smart launcher factor was also included in this algorithm to prevent trapping in local optimizations and promote network coverage in the routing process at the same time. The launcher prevents collision with obstacles in UAVs by adopting geometric techniques and the contour line. The performance of the proposed algorithm was compared with that of the most practical meta-heuristic algorithms (namely ACO and PSO methods). The findings revealed that the BOA algorithm compared to the other two algorithms had the lowest cost and the second lowest cost under the best and worst conditions, respectively. The findings also confirmed the better performance of BOA than the other two algorithms regarding execution time and the optimal value of the fit function.