This study proposes a novel hybrid strategy for formation control of a swarm of multiple unmanned aerial vehicles (UAVs). To enhance the fitness function of the formation, this research offers a three-dimensional formation control for a swarm using particle swarm optimization (PSO) with Cauchy mutant (CM) operators. We use CM operators to enhance the PSO algorithm by examining the varying fitness levels of the local and global optimal solutions for UAV formation control. We establish the terrain and the fixed-wing UAV model. Furthermore, it also models different control parameters of the UAV as well. The enhanced hybrid algorithm not only quickens the convergence rate but also improves the solution optimality. Lastly, we carry out the simulations for the multi-UAV swarm under terrain and radar threats and the simulation results prove that the hybrid method is effective and gives better fitness function.

这项研究提出了一种新颖的混合策略，用于控制多个无人机的编队控制。为了增强地层的适应性功能，本研究使用带有柯西突变体（CM）算子的粒子群优化（PSO）为群体提供了三维地层控制。我们使用CM运算符通过检查无人机编队控制的局部和全局最佳解决方案的变化适应度来增强PSO算法。我们建立地形和固定翼无人机模型。此外，它还可以对无人机的不同控制参数进行建模。改进的混合算法不仅加快了收敛速度，而且提高了求解的最优性。最后，