Abstract: A novel interference resource allocation method based on best neighborhood search algorithm (BNS) is proposed to address the interference resource allocation problem in cooperative jamming. Firstly, two fundamental neighborhood move operations are defined to ensure that adjacent states in the state space maintain a fixed hamming distance, thereby reducing the computational complexity of interference effectiveness evaluation. Subsequently, the best neighborhood search algorithm ensures strict monotonic increase of interference effectiveness, significantly improving the algorithm's convergence efficiency. Finally, by integrating the proposed weighted initial value generation strategy, the probability of achieving the global optimal solution is further enhanced. Simulation results demonstrate that compared to traditional swarm intelligence algorithms and reinforcement learning methods, the proposed approach exhibits superior convergence accuracy, faster convergence efficiency, and lower computational complexity, with particularly significant advantages in large-scale problem scenarios.