Optimization of Search and Rescue Paths for Submersibles Using Monte Carlo Simulations and Whale Optimization Algorithm

This paper addresses optimizing search and rescue (SAR) paths for submersibles using predictive models. The study first analyzes the forces acting on submersibles in varying conditions of temperature, salinity, and depth, establishing differential dynamic equations through Monte Carlo simulations to predict the motion trajectory with high probability. Comparison between single predictions and average motion trajectories shows that the likelihood of the average trajectory being within 200 meters of any single trajectory in three-dimensional space is less than 92.05%. The study evaluates the effectiveness of various underwater search equipment using the EWM-TOPSIS model, identifying the sonar system, automatic underwater robot, and multibeam underwater terrain detection system as critical components for enhancing SAR capabilities. Furthermore, the research simplifies SAR preparations by focusing on passive search strategies and optimizing search paths using the Whale Optimization Algorithm (WOA). By migrating and adjusting the model to different sea conditions, the study enhances prediction accuracy and SAR efficiency. The findings underscore the importance of combining probabilistic modeling with advanced optimization techniques to improve the reliability and effectiveness of submersible SAR operations.