Multi-UAV Target Allocation Approach Based on Improved Grey Wolf Optimizer

To address the problem of anti-ship target allocation for multiple unmanned aerial vehicles (UAVs), this paper proposes a collaborative strike decision-making scheme based on an improved grey wolf optimizer. First, a mathematical model for target allocation is constructed through a five-dimensional evaluation systemencompassing distance situation, angle situation, penetration probability, target value, and defensive threat, and a discrete coding mechanism is employed to solve the combinatorial optimization problem. Then, a situation-guided multi-leader collaborative update strategy with a nonlinear convergence factor is adopted to ensure the accuracy of target allocation, thereby enhancing the stability and convergence efficiency of the algorithm. Finally, small-scale encounter simulations are designed to compare the performance of the proposed improved algorithm with other optimization algorithms, and large-scale scenario experiments are conducted to verify its effectiveness. The simulation results demonstrate that the proposed improved algorithm offers significant advantages in the anti-ship target allocation problem. Its convergence speed and quality of allocation scheme surpass those of other optimization algorithms, highlighting its important application value in enhancing maritime precision strike effectiveness.