Research on deep-sea submarine position prediction and search and rescue strategy based on multi-intelligence optimization and machine learning

Abstract

In this paper, the position prediction and search and rescue strategy of deep-sea submarine are effectively studied by using multi-intelligence optimization and machine learning. First, the possible position of the submarine is predicted by building an accurate position prediction model, combining historical data and real-time ocean dynamic data. Secondly, NSGA-II, a multi-objective optimization algorithm, optimizes the combination of search and rescue equipment carried by search and rescue vessels, determines the best initial deployment point, designs an efficient search mode, and uses Bayesian probability algorithm to evaluate the rescue probability. Through these studies, this paper provides strong technical support for the safe operation and fault rescue of deep-sea submarines.