In a dynamic environment with wind forces and tornadoes, eliminating fluctuations and noise is critical to get the optimal results. Avoiding collision and simultaneous arrival of multiple unmanned aerial vehicles (multi-UAVs) is also a great problem. This paper addresses the cooperative path planning of multi-UAVs with in a dynamic environment. To deal with the aforementioned issues, we combine the maximum–minimum ant colony optimization (MMACO) and Cauchy Mutant (CM) operators to make a bio-inspired optimization algorithm. Our proposed algorithm eliminates the limitations of classical ant colony optimization (ACO) and MMACO, which has the issues of the slow convergence speed and a chance of falling into local optimum. This paper chooses the CM operator to enhance the MMACO algorithm by comparing and examining the varying tendency of fitness function of the local optimum position and the global optimum position when taking care of multi-UAVs path planning problems. It also makes sure that the algorithm picks the shortest route possible while avoiding collision. Additionally, the proposed method is more effective and efficient when compared to the classic MMACO. Finally, the simulation experiment results are performed under the dynamic environment containing wind forces and tornadoes.

中文翻译：

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基于最大最小蚁群优化和柯西突变算子的多无人机协同路径规划

在有风力和龙卷风的动态环境中，消除波动和噪音对于获得最佳结果至关重要。避免多架无人机（multi-UAV）的碰撞和同时到达也是一个很大的问题。本文讨论了多无人机在动态环境中的协同路径规划。为了解决上述问题，我们结合最大最小蚁群优化（MMACO）和柯西突变体（CM）算子来制作仿生优化算法。我们提出的算法消除了经典蚁群优化 (ACO) 和 MMACO 的局限性，后者具有收敛速度慢和容易陷入局部最优的问题。本文选择CM算子来增强MMACO算法，通过比较和检验局部最优位置和全局最优位置适应度函数在处理多无人机路径规划问题时的变化趋势。它还确保算法在避免碰撞的同时选择最短的路线。此外，与经典的 MMACO 相比，所提出的方法更加有效和高效。最后，在包含风力和龙卷风的动态环境下进行了仿真实验结果。与经典的MMACO相比，所提出的方法更加有效和高效。最后，在包含风力和龙卷风的动态环境下进行了仿真实验结果。与经典的MMACO相比，所提出的方法更加有效和高效。最后，在包含风力和龙卷风的动态环境下进行了仿真实验结果。