Research on rapid SAR solution for submarines based on Particle Swarm Optimization

Abstract

This paper uses particle swarm and neural network algorithms to construct an efficient submarine search and rescue (SAR) solution based on an unmanned underwater detector cluster, aiming to provide stronger safety guarantees for tourists who experience the submarine project. Specifically, this article constructs a position prediction model and a search optimization model respectively. The former will accurately simulate the submarine's motion trajectory and various parameters through a combination of interpolation fitting and neural network learning. The latter will use the above data to Particle swarm algorithm optimizes the group behavior of unmanned detectors to derive the optimal search and rescue plan. Finally, upon analyzing the success rate of this strategy, it’s surprised to find that its increasing trends complies with Logistic growth law. This indicates that the strategy has promising advantages in terms of time efficiency and accuracy. The research conducted in this article provides an application direction for the particle swarm algorithm and a feasible model for the construction of deep-sea search and rescue strategies, which has a great use value.