Improved Grey Wolf Optimization Algorithm

Aiming at the problems of slow convergence speed and easy to fall into local optimum of grey wolf optimization (GWO) in unmanned aerial vehicle 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. Secondly, in order to balance the developmental and exploratory capabilities of the algorithm, a cosine strategy is introduced to improve the calculation of the control factor. 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.

针对灰狼优化算法（Grey Wolf Optimization, GWO）在无人机路径规划中存在收敛速度缓慢、易陷入局部最优的问题，本文提出一种多策略融合的改进灰狼优化算法（Improved Grey Wolf Optimization, I-GWO）。该改进算法在初始化阶段引入种群对抗策略，以加速算法前期收敛速度；其次，为平衡算法的开发与探索能力，引入余弦策略优化控制因子的计算；同时，在位置更新阶段引入柯西分布逆累积分布函数与正切飞行算子，避免算法陷入局部最优而停滞。