Three dimensional trajectory planning of unmanned aerial vehicles based on quantum differential search

In this paper, an improved differential search (DS) algorithm was proposed to solve the trajectory planning problem. The DS algorithm is a new kind of evolutionary algorithm that develops rapidly recently. However, slow convergence speed and easily trapping into local optimum of the DS algorithm are the main disadvantages that limit its further application. To overcome the disadvantages of DS algorithm, we propose a quantum differential search (QDS) algorithm for solving the three dimensional trajectory planning of unmanned aerial vehicles (UAVs) in this paper. It is a combination of quantum theory and principles of differential search algorithm. By using the quantum strategy, the improved DS algorithm can escape the local optimum. The non-winner particles are mutated by quantum theory and the global best position is mutated by using the small extent of disturbance according to the variance ratio of fitness. Series of experimental results demonstrate the feasibility, effectiveness and robustness of our proposed method. The results show that the proposed QDS algorithm can effectively improve both the global searching ability and the speed of convergence.