Chaotic differential evolution approach for 3D trajectory planning of unmanned aerial vehicle

To overcome the disadvantage of low convergence speed and the premature convergence of differential evolution (DE), a chaotic DE was proposed. Aimed to improve the ability to break away from the local optimum and to find the global optimum, the non-winner particles were mutated by chaotic search and the global best position was mutated using the small extent of disturbance according to the variance ratio of fitness. Series of experimental comparison results are presented to show the feasibility, effectiveness and robustness of our proposed method. The results show that the proposed algorithm can effectively improve both the global searching ability and much better ability of avoiding pre-maturity.