Research On Ocean Meteorological Route Planning Based On Improved Particle Swarm Optimization Algorithm

In recent years,with the gradual enhancement of China’s international trade competitiveness,the total volume of maritime import and export trade has been continuously improved,and the ship transportation trade,which is mainly ocean transportation,has been vigorously developed.At present,the navigation of ships is gradually developing to information and precision.How to design the most convenient and efficient shipping routes has always been the subject and direction of continuous research in various countries.In the shipping design,the navigation route of the ship is mainly affected by meteorological and hydrological,sea surface environment and other aspects.In this paper,combined with the theoretical characteristics of particle swarm and immune algorithm,considering the influence of meteorological environment on shipping,on the basis of improving the inertia weight of particle swarm algorithm,an immune-particle swarm algorithm with improved inertial weight is designed,and applied to the ocean-going meteorological route planning simulation system,and the effectiveness of the algorithm is verified and analyzed,and the main research contents are as follows:（1）Establish the environmental mathematical model of ship route planning during sailing.Use the grid method and Mercator projection to plan the geographical environment of the map and analyze the calculation method of route indicator under mercator projection.Establish a mathematical model of discrete route to determine control variable information such as the number of segments,position variables,route variables,and speed variables.Focusing on the influence of wind and waves on ship speed,several common calculation methods of ship speed and critical speed under wind and wave environment are introduced,and the implementation method of meteorological model is determined.（2）An improved immune-particle swarm algorithm is proposed.The principle of the basic particle swarm is introduced,and by setting a fixed objective function,the solution of the objective function by the related parameter algorithm is studied.Among them,with inertia weight as the analysis point,the influence of different inertia weight settings on the optimal solution of particle swarm optimization is studied,and an improved inertia weight design method is proposed.Verify that using the improved inertia weight is better than other methods of inertia weights in the solution of global variables.After analyzing the advantages and disadvantages of the immune algorithm,an immune-particle swarm algorithm is designed.After the solution of the objective function,it is confirmed that the immune-particle swarm algorithm has faster convergence speed and better global searchability and process solving performance than the basic particle swarm algorithm.（3）A route planning algorithm for shipping routes is designed in a meteorological environment.On the basis of the environment model,the initial navigable area,objective function and fitness function are determined.Combining the optimized inertia weight with the immune-particle swarm algorithm,an immune-particle swarm algorithm with improved inertia weight is proposed,which is applied to the route planning and design of ships in the meteorological environment.The optimal solution set of the multi-objective function is determined by using the optimal solution of the Pareto frontier,and different weights are assigned to the objective function respectively,so as to obtain the comprehensive optimal solution of the multi-objective function.Finally,a visual simulation system is established,taking the sailing time and sailing risk as the objective functions.The immune-particle swarm algorithm with improved inertial weight and the basic particle swarm algorithm are respectively used for singletarget and multi-target navigation route planning.The improved algorithms are superior to the traditional particle swarm algorithm,and have good route design and planning capabilities.