Hybrid Chaos Mapping Improved Grey Wolf Optimisation Algorithm for UAV Path Planning

For the problem of path planning of unmanned aerial vehicles in a three-dimensional environment, this paper proposes a fusion chaos mapping improved gray wolf optimization algorithm. The position update function in the gray wolf algorithm is improved, which is more conducive to the transformation between the exploitation stage and the exploration stage of the wolf pack. The improved Sigmoid function is used as the nonlinear convergence factor to balance the global search and local search of the algorithm. In the chaotic mapping method, Tent chaotic mapping is used for population initialization, which improves population diversity, solution accuracy and convergence speed. Through tests on 8 CEC benchmark functions, this algorithm converges faster and has higher accuracy than the comparison algorithms. In the experimental simulation in the simulated mountain environment, among HTGWO, PSO, GWO and several variants of GWO, the path planned by the HTGWO algorithm has a low threat cost, is smooth and has fewer turning points.