# Research Progress and Prospects of Agricultural Aero-Bionic Technology in China
## 3 Biological Behavior and Agricultural Aviation
### Particle Swarm Optimization  
Particle swarm optimization (PSO) is an intelligent algorithm proposed to study the predation behavior of birds [54]. Birds search for food through cooperation among  
Figure 5: Flow chart of the ant colony algorithm.  
individuals. Each individual is regarded as a particle, with only position and velocity information. The particles follow the optimal particle to search in space. The particle swarm optimization algorithm first sets up the initial particle swarm and searches for the optimal solution through traversal. In each iteration, the particle will update its position and speed through the individual extreme value, Pbest, and the global extreme value, Gbest, and adjust its fitness value to find the optimal solution [55]. The process is shown in Figure 6.  
The particle swarm algorithm has the advantages of being a concise algorithm, easy implementation, good robustness, and fast convergence speed [56], but it is easy to fall into the local optimal solution and has a great impact on the search results [57]. In view of the problems of slow convergence of later calculations and the ease of falling into local optimal solutions when the particle swarm optimization is applied to the multi-aircraft cooperative task assignment of UAVs, [PERSON] [58] proposed a hybrid PSO-BAS (particle swarm optimization-beet antennae search) algorithm, in which the beetle antennae search algorithm originated from the foraging behavior of beetles. Beetles sense the intensity of food odors in the air through a pair of antennae on the top of their heads, and fly in the direction of the strong odor concentration. The beetle antennae search algorithm regards the food odor as a function. According to the difference of the value of the function in the three-dimensional space, the beetle explores the odor values of two points in the surrounding environment through two whiskers, until the optimal value is found. It combines the advantages of fast local convergence of the beetle antennae search algorithm with the particle swarm optimization algorithm to solve the problem of easily falling into a local optimal solution. The convergence speed of the algorithm is accelerated by adjusting the particle swarm inertia weight. The experimental results prove that the algorithm solves the multi-machine task allocation problem with fast convergence speed and high accuracy. [PERSON] [59] aimed at the slow convergence speed of traditional planning in complex environments and the tendency to fall into local optimal solutions. They proposed a repulsive field-particle swarm fusion algorithm to perform raster processing on obstacles in complex environments. The obstacle repulsion potential field constructs a function to  
Figure 6: Flow chart of particle swarm optimization.  
adjust the particle position and velocity to calculate the optimal path. The experimental results verify that the improved fusion algorithm is capable of obtaining the optimal solution of the optimization path with a faster convergence speed. [PERSON] [60] proposed a strategy combining inertia weight adjustment and jumping out of the local optimal solution for the optimization and real-time of UAV trajectory planning. This strategy can reduce the search space and improve the optimization accuracy and stability of the algorithm. The experimental results show that it is possible to generate more trajectories while satisfying online real-time trajectory planning.  
As a new intelligent bionic algorithm, particle swarm optimization has been widely used in engineering technology, scientific research, and other fields [61]. Compared with traditional algorithms, particle swarm optimization provides new ideas to solve the path planning problem in the complex environment of agricultural aviation.