# UAV Formation Trajectory Planning Algorithms: A Review
## 4 Intelligent Algorithm
### Heuristic Algorithm
#### 4.1.10 Gray Wolf Optimization Algorithm  
The Gray Wolf Optimization algorithm (GWO) is a new swarm intelligence optimization algorithm inspired by the predation behavior of gray wolves. The Gray Wolf Optimization algorithm divides gray wolf individual fitness into four different levels: optimal solution, suboptimal solution, third solution and candidate solution according to the calculated fitness; the individuals with the top three fitness guide other wolves towards the goal Iterative search, while continuously updating the solution level and position, until the best trajectory to the target point is found. Figure 20 contains a Gray Wolf Optimization algorithm position update model and a Gray Wolf Optimization algorithm (GWO) trajectory diagram.  
[PERSON] et al. [113] proposed a Bayesian algorithm based on Gray Wolf Optimization, which added dynamic Bayes and range-based value function (DBVF) into GWO to solve the trajectory planning and collision avoidance problems of multiple UAVs with fixed and moving obstacles in uncertain environments.  
[PERSON] et al. [114] proposed a multi-UAV trajectory planning method based on the Gray Wolf Optimization algorithm (GWO) to solve the problems of slow convergence, high trajectory calculation cost and local optimization of other meta-heuristic and deterministic algorithms in multi-UAV trajectory planning.  
[PERSON] et al. [115] proposed an improved Gray Wolf Optimization algorithm (GWO), which improved the population initialization, attenuation factor updating and single position updating of the Gray Wolf Optimization algorithm and solved the NP-hard problem of multi-UAV collaborative trajectory planning.  
[PERSON] et al. [116] proposed a trajectory planning method based on multi-population chaotic Gray Wolf Optimization (MP-CGWO). The multi-population concept and chaotic search strategy are added into the Gray Wolf Optimization algorithm (GWO), which solves the problem that the traditional GWO algorithm is easy to fall into local optimization.  
[PERSON] et al. [117] proposed a hybrid discrete intelligence algorithm (HDGWO) based on gray wolf optimizer. The algorithm uses the discrete gray wolf update operator and uses integer coding and a greedy algorithm to transform between the gray wolf space and the discrete problem space. Then, it adds the center position operation and the stagnation compensation gray wolf update operation, and finally, it adds an azimuth to improve the gray wolf algorithm, which solves the GWO problems of poor global convergence ability and local search ability.  
[PERSON] et al. [118] proposed an adaptive multi-UAV trajectory planning method to improve the Gray Wolf Optimization algorithm (AP-GWO). This method adds the spiral update position and self-adaptive adjustment mechanism to the Gray Wolf Optimization algorithm, which solves the problems of relatively long convergence time, relatively unsmooth trajectory and possibly not optimal trajectory of the traditional GWO algorithm.  
Figure 20: A position update model and GWO trajectory map. (**a**) A location updating model of the Gray Wolf Optimization algorithm; (**b**) Trajectory diagram of a Gray Wolf Optimization algorithm (black areas: obstacles; blue line: optimal trajectory).