Research on Multi-objective Task Assignment Scheme Based on Group Isomorphism UUV

The traditional ant colony algorithm is improved for the TSP extension problem when multi-objective task assignment is performed by a group homogeneous UUV. Based on the traditional ant colony algorithm and the TSP problem, the TSP problem model is extended to improve the initial information, the taboo table, the location state probability calculation formula, and the pheromone concentration update method to create an improved ant colony algorithm, and simulations are performed with three different evaluation functions: path, time, and path-time, respectively. The experimental results show that the optimal solution can be reached simultaneously by using path evaluation function and time evaluation function, and the solution is gradually optimized by using path-time evaluation function, and finally reaches the optimal solution. The improved ant colony algorithm not only solves the multi-individual and multi-objective task assignment, but also can significantly improve the work efficiency and effectively reduce the cost.