# Research Progress and Prospects of Agricultural Aero-Bionic Technology in China
## 3 Biological Behavior and Agricultural Aviation
### Ant Colony Optimization  
[PERSON] et al. [46] first discovered and proposed ant colony optimization. Ants have no vision, but they can find the optimal path. This is because ants release a pheromone along the path they walk, and move towards the path with high concentration according to the pheromone concentration on the way to find the optimal path. The process of ants searching for food is shown in Figure 4. The ant colony algorithm is a bionic algorithm proposed through the collective path-finding behavior of ants, and the algorithm mainly includes probability criteria and a pheromone update mechanism. In the algorithm, the ants will judge the probability of going to the next node based on the values of the initial pheromone and heuristic information. After all ants have completed a traversal, the pheromone concentration on the path will be updated. They repeat the above steps until the optimal path is found. The flow chart of the ant colony algorithm is shown in Figure 5. The ant colony algorithm can quickly find the local optimal solution. Compared with other algorithms, it has better time performance [47], search stability, and high accuracy [48]. However, there is random probability and poor pheromone interference in the operation of the algorithm, which will lead to the low quality of the solution [49], and the algorithm has problems such as insufficient initial information, slow convergence speed, and ease of falling into local optimal solutions [50].  
Figure 4: Ant foraging process: (**a**) early path search, (**b**) mid-route search, and (**c**) optimal path.  
Agricultural aviation, especially plant protection UAVs, must face a complex operating environment during the operation. The application of the ant colony algorithm can effectively improve the efficiency of UAVs. Considering the limitations of the ant colony algorithm, it is usually combined with other algorithms to improve the convergence speed of the algorithm and reduce the likelihood of falling into a local optimal solution. [PERSON] [51] proposed an improved ant colony algorithm for the operation area of plant protection UAVs with irregular polygons and internal obstacles, discretized the plane operation path to generate a three-dimensional path, and then reasonably planned the path points in the path. The experimental results show that the optimization path of the algorithm is significantly improved compared with the greedy algorithm and has certain practicability. [PERSON] [52] analyzed the ant colony algorithm through grid environment modeling, and proposed an ant colony opposite search strategy and ant backtracking strategy to improve the search efficiency of the algorithm, while avoiding the algorithm from falling into a U-shaped trap, and redefining the pheromone. The experimental results show that compared with the traditional ant colony algorithm, the improved ant colony algorithm has a faster convergence speed and an optimal path. [PERSON] [53] proposed an intelligent combination algorithm combining the A* algorithm and ant colony algorithm for complex low-altitude environments. The combination algorithm adds the A* algorithm to overcome the problems of slow convergence and falling into a local optimal solution of the ant colony algorithm. The global optimal path is found through the A* algorithm, and then the local optimal path is planned in combination with the ant colony algorithm. The experimental results show that the combined algorithm can not only improve the speed of path optimization, but also avoid obstacles.  
The ant colony algorithm was originally proposed to solve the traveling salesman problem. With the development of science and technology, it has been gradually applied to optimization problems in various fields. In the spraying operation of plant protection UAVs, the application of the ant colony algorithm can effectively reduce energy consumption costs, improve efficiency, and help to promote the development of precision agricultural aviation.