The rapid development of the global shipping industry and the changes in complex marine environments have put forward higher requirements for ship formation and route optimization. The purpose of the research is to improve the efficiency and accuracy of ship formation and route planning through improved algorithms. Based on this, a ship formation model combining improved particle swarm optimization algorithm and a generative route optimization method based on improved Douglas-Peucker algorithm are proposed. The particle swarm algorithm introduces dynamic adaptive parameter adjustment and the cross mutation strategy of genetic algorithm, while the Douglas-Peucker algorithm integrates density-based noise application spatial clustering algorithm to improve model performance. The test results show that the total navigation distance of the allocation path generated by the ship formation model is 605.3 meters, the calculation time is 31.8 seconds, and all ships can be accurately allocated to the target point. When the number of iterations is 1000, the route optimization model has a route coverage rate of 95.9% on the training set, an average error of 30.5 meters, and a computation time of 45.9 seconds, achieving zero collisions. The experimental results show that the improved algorithm outperforms traditional methods in accuracy and stability of formation target allocation and route planning, especially under complex sea conditions, and can significantly reduce computation time and errors. The research provides a new technological means for optimizing ship formations and routes, which has certain application potential and practical value.