Research on Deep Bomb Delivery Strategy Based on Monte Carlo Simulation and Adaptive Optimization Algorithm - Huang, Lingkai (Transactions on Computer Science and Intelligent Systems Research, 2024)
Aiming at the problem of insufficient precision of deep bomb delivery in traditional anti-submarine warfare, this paper proposes a novel algorithm combining Monte Carlo simulation and adaptive optimization [1]. The algorithm optimises the deep bomb delivery strategy by defining the probability distribution of the submarine position. Specifically, Monte Carlo simulation is used to generate samples of possible submarine positions, analyse the effect of the submarine's three-dimensional positional error underwater on the probability of hitting a single depth charge, and explore the role of the dual-fuse detonation mechanism [2]. On this basis, the submarine's orientation error in 3D space is further considered, and an optimisation model is used to determine the optimal fixed-depth fuse detonation depth to maximise the hit probability of a single deep bomb [3]. In addition, this paper extends to the case of multiple depth charges, and optimises the overall hit probability by designing the array of charges and plane spacing to ensure that at least one depth charge can hit the submarine [4]. The experimental results show that the algorithm significantly improves the hit probability of the deep bomb, effectively reduces the impact of positioning error on the combat effect and demonstrates its potential application in practical anti-submarine warfare.