Optimization of Cooperative Countermeasure Strategy for Underwater Acoustic Deception Devices Based on Adaptive Mutation PSO

To address the insufficient research on the cooperative combat system of multiple underwater acoustic deception devices when surface ships defend against underwater guidance devices, as well as the problems of low efficiency and poor portability of traditional exhaustive methods, this paper introduced the particle swarm optimization(PSO) algorithm to optimize the countermeasure model and improved the algorithm by introducing an adaptive inertia weight and a multi-radius mutation mechanism. Meanwhile, the paper established a multi-objective optimization function with core indicators including defense success rate, minimum engagement distance, and ship survival time to optimize the ship’s evasive course, as well as the launch distance and angle of two flying-aided underwater acoustic deception devices. Simulation results show that the proposed improved algorithm has higher efficiency, faster convergence speed, and better fitness compared with traditional algorithms. It also reveals the differences in optimal countermeasure strategies under different bearing angle situations and their tactical values, providing an important reference for the formulation of defense strategies against underwater guidance devices.