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，以加快算法前期收敛速度；其次，为平衡算法的开发与探索能力，引入余弦策略对控制因子的计算方式进行优化；同时，在位置更新阶段引入柯西分布逆累积分布函数（Cauchy distribution inverse cumulative distribution function）与切线飞行算子，避免算法陷入局部最优而出现停滞。

搜集汇总

背景与挑战

背景概述

该数据集涉及一个改进的灰狼优化算法（I-GWO），专为优化无人机路径规划而设计。它通过融合多种策略，包括种群对抗、余弦控制因子调整以及柯西分布和正切飞行算子，旨在解决原始算法收敛慢和易陷入局部最优的问题，从而提升算法的整体性能和鲁棒性。