Improved Grey Wolf Optimization Algorithm for UAV Path Planning

Aiming at the problems of slow convergence speed and easy to fall into local optimum of grey wolf optimization in unmanned aerial vehicle (UAV) path planning, an improved grey wolf optimization (I-GWO) with multi-strategy fusion is proposed. The improved algorithm adds a population confrontation strategy in the initialization stage to accelerate the speed of convergence in the first period and optimizes the calculation of control factors to improve the accuracy of algorithm convergence. Meanwhile, the Cauchy distribution inverse cumulative distribution function and tangent flight operator are introduced in the position updating phase to prevent the algorithm from stagnating at the local optimum. The experimental results show that the I-GWO has higher convergence accuracy and speed compared to other algorithms.