A force grey wolf optimizer algorithm for unmanned aerial vehicle trajectory planning

A force grey wolf optimization (FGWO) algorithm is proposed in this paper. The FGWO algorithm is used to deal with the local optimum and premature convergence problems in unmanned aerial vehicle (UAV) trajectory planning. Firstly, the mathematical model of environmental trajectory planning is built, and the weighting of both track distance and threat constraint is used as the cost function. Secondly, a nonlinear convergence factor is introduced to balance the global exploration and local exploitation capability of the GWO algorithm. In addition, a new search mechanism is introduced in the late iteration by weighting to enhance the merit -seeking ability. Finally, the levy -flight and greedy strategies are applied to make the algorithm avoid local optimality and thus obtain optimal solutions. The results of simulation show that the proposed FGWO algorithm can get better results in function testing and trajectory planning.