LAUSR

By improving the efficiency of the long-range maritime search and rescue (LRMSAR), this paper uses the independence of GIS and optimization algorithm to establish calculation module respectively. Firstly, the GIS is used to project the geographic coordinates of past merchant vessels and alternative islands where the maritime search and rescue (SAR) base can be built. Secondly, the K-means clustering algorithm is used to obtain the location of the SAR dynamic duty points. Then, the obtained coordinate data of the alternative islands and the SAR dynamic duty points are input to the optimization algorithm module. Based on the traditional plant growth simulation algorithm, a multi-objective plant growth simulation algorithm is designed by adding the key technologies of fast non-dominated sorting, dominant strength, crowding distance, elite strategy, and partially ordered set. The optimal scheme considering the position of the SAR dynamic duty points, the allocation of SAR vessels and the location of SAR bases is obtained. Finally, taking the construction of SAR base in Spratly Islands of the South China Sea and the allocation of SAR ships as an example, the better results are obtained. To verify the effectiveness of the model and the algorithm in this study, different scales calculating examples were selected for comparison. The calculation results show that the improved algorithm is better in terms of optimization result, solution distribution and adaptability. The research shows that combining the location of SAR bases, the allocation of SAR resources and the spatial analysis function of the GIS for comprehensive optimization can effectively make up for the deficiencies of previous studies and obtain a better optimization scheme. The research results of this study can provide new analysis methods and optimization scheme for the location of SAR bases and the allocation of SAR vessels in remote oceans.