# Research Progress and Prospects of Agricultural Aero-Bionic Technology in China
## 3 Biological Behavior and Agricultural Aviation
### Genetic Algorithm  
The genetic algorithm is a global optimization intelligent algorithm, which is inspired by [PERSON]'s evolution theory. Based on the mechanism of natural genetics and natural selection, it uses crossover, mutation, and selection to simulate the process of biological evolution. The core steps in the algorithm mainly include coding, initialization, selection, crossover, and mutation. Firstly, the solution problem is coded and transformed into the solution space of the genetic algorithm. Each code is a feasible solution to form an initial population. Secondly, the individual is evaluated according to the principle of survival of the fittest. Their individual fitness is calculated, and finally, high-quality individuals are selected for cross-mutation to generate a new generation of high-quality solution populations, and the above steps are repeated until the optimal solution is generated [62]. The flow of genetic algorithm operation is shown in Figure 7. The genetic algorithm is a multi-point search algorithm, which can calculate and optimize complex multi-objectives at the same time, avoid falling into local minimum points, and ensure the global optimization ability [63]. The genetic algorithm has fast calculation in the early stage, but large amounts of redundant iterations in the later stage, which is not suitable for real-time trajectory planning [64].  
Figure 7: Flow chart of the genetic algorithm.  
At present, many scholars have put forward improvement strategies to solve the problems of slow convergence speed and the ease of falling into local optimization in the later stage of the genetic algorithm. [PERSON] [65] proposed an improved genetic algorithm, the simulated annealing algorithm, which does not easily fall into the local optimal solution. This feature is integrated into the genetic algorithm to better solve the local optimal problem, and the directional mutation is introduced to speed up the search speed of the optimal solution of the genetic algorithm. [PERSON] [66] improved the three operators of selection, crossover, and mutation in the genetic algorithm. Aiming at the premature convergence of the algorithm, a hybrid non-multiple string selection operator was proposed. In order to improve the convergence effect of the algorithm, an asymmetric mapping crossover operator was proposed. Through the heuristic multiple mutation operator, the premature phenomenon of the algorithm is suppressed. The results show that the improved algorithm has good environmental adaptability. [PERSON] [67] proposed a global planning algorithm for plant protection UAVs for multi-regional operations of plant protection UAVs. The algorithm is divided into three parts: single-region route planning, inter-regional operation sequence planning, and inter-regional route scheduling planning. The improved genetics algorithm mainly focuses on the regional operation sequence and inter-regional route planning. The optimal operation area sequence is first solved, and then the optimal inter-area scheduling route is obtained through the improved genetic algorithm. The results show that the algorithm effectively realizes the global planning and improves the operational efficiency of plant protection UAVs.  
At present, the research hotspots of the genetic algorithm mainly focus on the improvement of the algorithm. Based on the characteristics of the genetic algorithm being suitable for global search and strong robustness, it can be better applied to agricultural aviation operations in complex environments, and the algorithm is easy to combine with other algorithms [68], which can provide a more suitable fusion algorithm for different plant protection operation requirements.